Question: Supervised learning of distance metrics by maximizing the Jeffrey divergence.

This paper delves into the principle of least squares quantization in pulse-code modulation (PCM), a critical technique for handling a specific ensemble of signals. It is widely acknowledged that in PCM systems, the spacing of quantum values should be adjusted according to the probability of signal amplitude occurrences, ensuring denser spacing in regions where amplitudes are more likely to fall. Building upon the foundational work of Panter and Dite, who demonstrated that as the number of quanta approaches infinity, the asymptotic fractional density of quanta per unit voltage follows the one-third power of the probability density per unit voltage of signal amplitudes, this study explores the optimal quantization scheme for any finite number of quanta. Necessary conditions are derived for the quanta and quantization intervals that must be met in an optimal finite quantization scheme, with the aim of minimizing the average quantization noise power. The findings of this paper converge with the Panter and Dite result as the number of quanta increases significantly. Numerical data is provided for the optimal quantization schemes for 26 quanta, b = 1,2, ...,7, under both Gaussian and Laplacian distributions of signal amplitudes, illustrating the practical application of these theoretical insights.

Domain adaptation is transfer learning which aims to generalize a learning model across training and testing data with different distributions. Most previous research tackle this problem in seeking a shared feature representation between source and target domains while reducing the mismatch of their data distributions. In this paper, we propose a close yet discriminative domain adaptation method, namely CDDA, which generates a latent feature representation with two interesting properties. First, the discrepancy between the source and target domain, measured in terms of both marginal and conditional probability distribution via Maximum Mean Discrepancy is minimized so as to attract two domains close to each other. More importantly, we also design a repulsive force term, which maximizes the distances between each label dependent sub-domain to all others so as to drag different class dependent sub-domains far away from each other and thereby increase the discriminative power of the adapted domain. Moreover, given the fact that the underlying data manifold could have complex geometric structure, we further propose the constraints of label smoothness and geometric structure consistency for label propagation. Extensive experiments are conducted on 36 cross-domain image classification tasks over four public datasets. The Comprehensive results show that the proposed method consistently outperforms the state-of-the-art methods with significant margins. ∗These first two authors contributed equally.

Compatibility between items, such as clothes and shoes, is a major factor among customer’s purchasing decisions. However, learning “compatibility” is challenging due to (1) broader notions of compatibility than those of similarity, (2) the asymmetric nature of compatibility, and (3) only a small set of compatible and incompatible items are observed. We propose an end-to-end trainable system to embed each item into a latent vector and project a query item into K compatible prototypes in the same space. These prototypes reflect the broad notions of compatibility. We refer to both the embedding and prototypes as “Compatibility Family”. In our learned space, we introduce a novel Projected Compatibility Distance (PCD) function which is differentiable and ensures diversity by aiming for at least one prototype to be close to a compatible item, whereas none of the prototypes are close to an incompatible item. We evaluate our system on a toy dataset, two Amazon product datasets, and Polyvore outfit dataset. Our method consistently achieves state-of-the-art performance. Finally, we show that we can visualize the candidate compatible prototypes using a Metric-regularized Conditional Generative Adversarial Network (MrCGAN), where the input is a projected prototype and the output is a generated image of a compatible item. We ask human evaluators to judge the relative compatibility between our generated images and images generated by CGANs conditioned directly on query items. Our generated images are significantly preferred, with roughly twice the number of votes as others.

Covariate shift is an unconventional learning scenario in which training and testing data have different distributions. A general principle to solve the problem is to make the training data distribution similar to that of the test domain, such that classifiers computed on the former generalize well to the latter. Current approaches typically target on sample distributions in the input space, however, for kernel-based learning methods, the algorithm performance depends directly on the geometry of the kernel-induced feature space. Motivated by this, we propose to match data distributions in the Hilbert space, which, given a pre-defined empirical kernel map, can be formulated as aligning kernel matrices across domains. In particular, to evaluate similarity of kernel matrices defined on arbitrarily different samples, the novel concept of surrogate kernel is introduced based on the Mercer’s theorem. Our approach caters the model adaptation specifically to kernel-based learning mechanism, and demonstrates promising results on several real-world applications. Proceedings of the 30 th International Conference on Machine Learning, Atlanta, Georgia, USA, 2013. JMLR: W&CP volume 28. Copyright 2013 by the author(s).

Three Bayesian ideas are presented for supervised adaptive classifiers. First, it is argued that the output of a classifier should be obtained by marginalizing over the posterior distribution of the parameters; a simple approximation to this integral is proposed and demonstrated. This involves a "moderation" of the most probable classifier's outputs, and yields improved performance. Second, it is demonstrated that the Bayesian framework for model comparison described for regression models in MacKay (1992a,b) can also be applied to classification problems. This framework successfully chooses the magnitude of weight decay terms, and ranks solutions found using different numbers of hidden units. Third, an information-based data selection criterion is derived and demonstrated within this framework.

The primary premise upon which top-N recommender systems operate is that similar users are likely to have similar tastes with regard to their product choices. For this reason, recommender algorithms depend deeply on similarity metrics to build the recommendation lists for end-users.
 However, it has been noted that the products offered on recommendation lists are often too similar to each other and attention has been paid towards the goal of improving diversity to avoid monotonous recommendations.
 Noting that the retrieval of a set of items matching a user query is a common problem across many applications of information retrieval, we model the competing goals of maximizing the diversity of the retrieved list while maintaining adequate similarity to the user query as a binary optimization problem. We explore a solution strategy to this optimization problem by relaxing it to a trust-region problem.This leads to a parameterized eigenvalue problem whose solution is finally quantized to the required binary solution. We apply this approach to the top-N prediction problem, evaluate the system performance on the Movielens dataset and compare it with a standard item-based top-N algorithm. A new evaluation metric ItemNovelty is proposed in this work. Improvements on both diversity and accuracy are obtained compared to the benchmark algorithm.

We compare eleven methods for finding prototypes upon which to base the nearest Ž prototype classifier. Four methods for prototype selection are discussed: Wilson Hart a . condensation error-editing method , and three types of combinatorial search random search, genetic algorithm, and tabu search. Seven methods for prototype extraction are discussed: unsupervised vector quantization, supervised learning vector quantization Ž . with and without training counters , decision surface mapping, a fuzzy version of vector quantization, c-means clustering, and bootstrap editing. These eleven methods can be usefully divided two other ways: by whether they employ preor postsupervision; and by whether the number of prototypes found is user-defined or ‘‘automatic.’’ Generalization error rates of the 11 methods are estimated on two synthetic and two real data sets. Offering the usual disclaimer that these are just a limited set of experiments, we feel confident in asserting that presupervised, extraction methods offer a better chance for success to the casual user than postsupervised, selection schemes. Finally, our calculations do not suggest that methods which find the ‘‘best’’ number of prototypes ‘‘automatically’’ are superior to methods for which the user simply specifies the number of prototypes. 2001 John Wiley & Sons, Inc.

We propose a new technique for direct visual matching of images for the purposes of face recognition and image retrieval, using a probabilistic measure of similarity, based primarily on a Bayesian (MAP) analysis of image differences. The performance advantage of this probabilistic matching technique over standard Euclidean nearest-neighbor eigenface matching was demonstrated using results from DARPA’s 1996 “FERET” face recognition competition, in which this Bayesian matching algorithm was found to be the top performer. In addition, we derive a simple method of replacing costly computation of nonlinear (on-line) Bayesian similarity measures by inexpensive linear (off-line) subspace projections and simple Euclidean norms, thus resulting in a significant computational speed-up for implementation with very large databases. Appears in: Pattern Recognition, Vol. 33, No. 11, pps. 1771-1782, November, 2000. This work may not be copied or reproduced in whole or in part for any commercial purpose. Permission to copy in whole or in part without payment of fee is granted for nonprofit educational and research purposes provided that all such whole or partial copies include the following: a notice that such copying is by permission of Mitsubishi Electric Research Laboratories, Inc.; an acknowledgment of the authors and individual contributions to the work; and all applicable portions of the copyright notice. Copying, reproduction, or republishing for any other purpose shall require a license with payment of fee to Mitsubishi Electric Research Laboratories, Inc. All rights reserved. Copyright c Mitsubishi Electric Research Laboratories, Inc., 2002 201 Broadway, Cambridge, Massachusetts 02139 Appears in: Pattern Recognition, Vol. 33, No. 11, pps. 1771-1782, November, 2000.

In entity matching, a fundamental issue while training a classifier to label pairs of entities as either duplicates or non-duplicates is the one of selecting informative training examples. Although active learning presents an attractive solution to this problem, previous approaches minimize the misclassification rate (0-1 loss) of the classifier, which is an unsuitable metric for entity matching due to class imbalance (i.e., many more non-duplicate pairs than duplicate pairs). To address this, a recent paper [1] proposes to maximize recall of the classifier under the constraint that its precision should be greater than a specified threshold. However, the proposed technique requires the labels of all n input pairs in the worst-case.
 Our main result is an active learning algorithm that approximately maximizes recall of the classifier while respecting a precision constraint with provably sub-linear label complexity (under certain distributional assumptions). Our algorithm uses as a black-box any active learning module that minimizes 0-1 loss. We show that label complexity of our algorithm is at most log n times the label complexity of the black-box, and also bound the difference in the recall of classifier learnt by our algorithm and the recall of the optimal classifier satisfying the precision constraint. We provide an empirical evaluation of our algorithm on several real-world matching data sets that demonstrates the effectiveness of our approach.

We consider the problem of density estimation on Riemannian manifolds. Density estimation on manifolds has many applications in fluid-mechanics, optics and plasma physics and it appears often when dealing with angular variables (such as used in protein folding, robot limbs, gene-expression) and in general directional statistics. In spite of the multitude of algorithms available for density estimation in the Euclidean spaces R that scale to large n (e.g. normalizing flows, kernel methods and variational approximations), most of these methods are not immediately suitable for density estimation in more general Riemannian manifolds. We revisit techniques related to homeomorphisms from differential geometry for projecting densities to sub-manifolds and use it to generalize the idea of normalizing flows to more general Riemannian manifolds. The resulting algorithm is scalable, simple to implement and suitable for use with automatic differentiation. We demonstrate concrete examples of this method on the n-sphere S. In recent years, there has been much interest in applying variational inference techniques to learning large scale probabilistic models in various domains, such as images and text [1, 2, 3, 4, 5, 6]. One of the main issues in variational inference is finding the best approximation to an intractable posterior distribution of interest by searching through a class of known probability distributions. The class of approximations used is often limited, e.g., mean-field approximations, implying that no solution is ever able to resemble the true posterior distribution. This is a widely raised objection to variational methods, in that unlike MCMC, the true posterior distribution may not be recovered even in the asymptotic regime. To address this problem, recent work on Normalizing Flows [7], Inverse Autoregressive Flows [8], and others [9, 10] (referred collectively as normalizing flows), focused on developing scalable methods of constructing arbitrarily complex and flexible approximate posteriors from simple distributions using transformations parameterized by neural networks, which gives these models universal approximation capability in the asymptotic regime. In all of these works, the distributions of interest are restricted to be defined over high dimensional Euclidean spaces. There are many other distributions defined over special homeomorphisms of Euclidean spaces that are of interest in statistics, such as Beta and Dirichlet (n-Simplex); Norm-Truncated Gaussian (n-Ball); Wrapped Cauchy and Von-Misses Fisher (n-Sphere), which find little applicability in variational inference with large scale probabilistic models due to the limitations related to density complexity and gradient computation [11, 12, 13, 14]. Many such distributions are unimodal and generating complicated distributions from them would require creating mixture densities or using auxiliary random variables. Mixture methods require further knowledge or tuning, e.g. number of mixture components necessary, and a heavy computational burden on the gradient computation in general, e.g. with quantile functions [15]. Further, mode complexity increases only linearly with mixtures as opposed to exponential increase with normalizing flows. Conditioning on auxiliary variables [16] on the other hand constrains the use of the created distribution, due to the need for integrating out the auxiliary factors in certain scenarios. In all of these methods, computation of low-variance gradients is difficult due to the fact that simulation of random variables cannot be in general reparameterized (e.g. rejection sampling [17]). In this work, we present methods that generalizes previous work on improving variational inference in R using normalizing flows to Riemannian manifolds of interest such as spheres S, tori T and their product topologies with R, like infinite cylinders. ar X iv :1 61 1. 02 30 4v 1 [ st at .M L ] 7 N ov 2 01 6 Figure 1: Left: Construction of a complex density on S by first projecting the manifold to R, transforming the density and projecting it back to S. Right: Illustration of transformed (S → R) densities corresponding to an uniform density on the sphere. Blue: empirical density (obtained by Monte Carlo); Red: Analytical density from equation (4); Green: Density computed ignoring the intrinsic dimensionality of S. These special manifolds M ⊂ R are homeomorphic to the Euclidean space R where n corresponds to the dimensionality of the tangent space of M at each point. A homeomorphism is a continuous function between topological spaces with a continuous inverse (bijective and bicontinuous). It maps point in one space to the other in a unique and continuous manner. An example manifold is the unit 2-sphere, the surface of a unit ball, which is embedded in R and homeomorphic to R (see Figure 1). In normalizing flows, the main result of differential geometry that is used for computing the density updates is given by, d~x = |det Jφ| d~u and represents the relationship between differentials (infinitesimal volumes) between two equidimensional Euclidean spaces using the Jacobian of the function φ : R → R that transforms one space to the other. This result only applies to transforms that preserve the dimensionality. However, transforms that map an embedded manifold to its intrinsic Euclidean space, do not preserve the dimensionality of the points and the result above become obsolete. Jacobian of such transforms φ : R → R with m > n are rectangular and an infinitesimal cube on R maps to an infinitesimal parallelepiped on the manifold. The relation between these volumes is given by d~x = √ det G d~u, where G = J φ Jφ is the metric induced by the embedding φ on the tangent space TxM, [18, 19, 20]. The correct formula for computing the density over M now becomes : ∫

Many multimedia applications can benefit from techniques for adapting existing classifiers to data with different distributions. One example is cross-domain video concept detection which aims to adapt concept classifiers across various video domains. In this paper, we explore two key problems for classifier adaptation: (1) how to transform existing classifier(s) into an effective classifier for a new dataset that only has a limited number of labeled examples, and (2) how to select the best existing classifier(s) for adaptation. For the first problem, we propose Adaptive Support Vector Machines (A-SVMs) as a general method to adapt one or more existing classifiers of any type to the new dataset. It aims to learn the "delta function" between the original and adapted classifier using an objective function similar to SVMs. For the second problem, we estimate the performance of each existing classifier on the sparsely-labeled new dataset by analyzing its score distribution and other meta features, and select the classifiers with the best estimated performance. The proposed method outperforms several baseline and competing methods in terms of classification accuracy and efficiency in cross-domain concept detection in the TRECVID corpus.


Question: Supervised learning of distance metrics by maximizing the Jeffrey divergence.

This paper presents an innovative sparse metric learning technique for high-dimensional data through l1-penalized log-determinant regularization. Unlike conventional distance metric learning methods, the proposed algorithm capitalizes on the inherent sparsity in high-dimensional feature spaces. This sparsity-driven approach to learning distance metrics serves to mitigate the complexity of the distance model, particularly in scenarios characterized by a limited number of examples and high feature dimensions. Drawing parallels with the covariance estimation problem, we establish that the proposed distance learning algorithm yields a consistent outcome, converging to the target distance matrix at a rate of O(√m2 log d)/n), with a maximum of m non-zero elements per row. From a computational standpoint, the l1-penalized log-determinant formulation enables efficient optimization through a block coordinate descent method, which is notably faster than the standard semi-definite programming approach commonly employed in other advanced distance learning algorithms. Comparative analysis against leading methods on diverse datasets demonstrates the competitiveness of our algorithm.

**Question**: A Multi-Objective Evolutionary Algorithm for Solving the Traveling Salesman Problem

**Documents**: An evolutionary multi-objective optimization approach for the traveling salesman problem with profits
This paper presents a new multi-objective evolutionary algorithm (MOEA) for solving the traveling salesman problem with profits (TSPP). The TSPP is an NP-hard problem which has received considerable attention in the literature. The aim of this problem is to find a tour that maximizes the profit while not exceeding a given travel budget. We propose a new MOEA that uses a set of non-dominated solutions to approximate the Pareto front of the TSPP. The proposed algorithm uses a population-based approach and a tournament selection mechanism to maintain diversity in the population. We also introduce a new crossover operator that is specifically designed for the TSPP. The performance of the proposed algorithm is compared with that of other well-known MOEAs on a set of benchmark problems. The results show that the proposed algorithm is able to find solutions that are close to the Pareto front and that it outperforms the other MOEAs in terms of the hypervolume indicator.

**Similar Documents**: This article introduces an innovative multi-objective evolutionary algorithm (MOEA) tailored for the resolution of the traveling salesman problem with profits (TSPP), a problem that has garnered significant scholarly interest due to its NP-hard complexity. The central objective of the TSPP is to identify a tour that maximizes profit, all while adhering to a specified travel budget. Our contribution is a novel MOEA that leverages a

Learning can be made more efficient if we can actively select particularly salient data points. Within a Bayesian learning framework, objective functions are discussed that measure the expected informativeness of candidate measurements. Three alternative specifications of what we want to gain information about lead to three different criteria for data selection. All these criteria depend on the assumption that the hypothesis space is correct, which may prove to be their main weakness.

INTRODUCTION
K-nearest neighbor (k-NN) classification is conventional non-parametric classifier, which has been used as the baseline classifier in many pattern classification problems. It is based on measuring the distances between the test data and each of the training data to decide the final classification output.

CASE DESCRIPTION
Since the Euclidean distance function is the most widely used distance metric in k-NN, no study examines the classification performance of k-NN by different distance functions, especially for various medical domain problems. Therefore, the aim of this paper is to investigate whether the distance function can affect the k-NN performance over different medical datasets. Our experiments are based on three different types of medical datasets containing categorical, numerical, and mixed types of data and four different distance functions including Euclidean, cosine, Chi square, and Minkowsky are used during k-NN classification individually.

DISCUSSION AND EVALUATION
The experimental results show that using the Chi square distance function is the best choice for the three different types of datasets. However, using the cosine and Euclidean (and Minkowsky) distance function perform the worst over the mixed type of datasets.

CONCLUSIONS
In this paper, we demonstrate that the chosen distance function can affect the classification accuracy of the k-NN classifier. For the medical domain datasets including the categorical, numerical, and mixed types of data, K-NN based on the Chi square distance function performs the best.

Cross-modal similarity search is a problem about designing a search system supporting querying across content modalities, e.g., using an image to search for texts or using a text to search for images. This paper presents a compact coding solution for efficient search, with a focus on the quantization approach which has already shown the superior performance over the hashing solutions in the single-modal similarity search. We propose a cross-modal quantization approach, which is among the early attempts to introduce quantization into cross-modal search. The major contribution lies in jointly learning the quantizers for both modalities through aligning the quantized representations for each pair of image and text belonging to a document. In addition, our approach simultaneously learns the common space for both modalities in which quantization is conducted to enable efficient and effective search using the Euclidean distance computed in the common space with fast distance table lookup. Experimental results compared with several competitive algorithms over three benchmark datasets demonstrate that the proposed approach achieves the state-of-the-art performance.

Non-parametric models and techniques enjoy a growing popularity in the field of machine learning, and among these Bayesian inference for Gaussian process (GP) models has recently received significant attention. We feel that GP priors should be part of the standard toolbox for constructing models relevant to machine learning in the same way as parametric linear models are, and the results in this thesis help to remove some obstacles on the way towards this goal. In the first main chapter, we provide a distribution-free finite sample bound on the difference between generalisation and empirical (training) error for GP classification methods. While the general theorem (the PAC-Bayesian bound) is not new, we give a much simplified and somewhat generalised derivation and point out the underlying core technique (convex duality) explicitly. Furthermore, the application to GP models is novel (to our knowledge). A central feature of this bound is that its quality depends crucially on task knowledge being encoded faithfully in the model and prior distributions, so there is a mutual benefit between a sharp theoretical guarantee and empirically well-established statistical practices. Extensive simulations on real-world classification tasks indicate an impressive tightness of the bound, in spite of the fact that many previous bounds for related kernel machines fail to give non-trivial guarantees in this practically relevant regime. In the second main chapter, sparse approximations are developed to address the problem of the unfavourable scaling of most GP techniques with large training sets. Due to its high importance in practice, this problem has received a lot of attention recently. We demonstrate the tractability and usefulness of simple greedy forward selection with information-theoretic criteria previously used in active learning (or sequential design) and develop generic schemes for automatic model selection with many (hyper)parameters. We suggest two new generic schemes and evaluate some of their variants on large real-world classification and regression tasks. These schemes and their underlying principles (which are clearly stated and analysed) can be applied to obtain sparse approximations for a wide regime of GP models far beyond the special cases we studied here.

Kernel-based learning algorithms work by embedding the data into a Euclidean space, and then searching for linear relations among the embedded data points. The embedding is performed implicitly, by specifying the inner products between each pair of points in the embedding space. This information is contained in the so-called kernel matrix, a symmetric and positive definite matrix that encodes the relative positions of all points. Specifying this matrix amounts to specifying the geometry of the embedding space and inducing a notion of similarity in the input space—classical model selection problems in machine learning. In this paper we show how the kernel matrix can be learned from data via semi-definite programming (SDP) techniques. When applied to a kernel matrix associated with both training and test data this gives a powerful transductive algorithm— using the labelled part of the data one can learn an embedding also for the unlabelled part. The similarity between test points is inferred from training points and their labels. Importantly, these learning problems are convex, so we obtain a method for learning both the model class and the function without local minima. Furthermore, this approach leads directly to a convex method to learn the 2-norm soft margin parameter in support vector machines, solving another important open problem. Finally, the novel approach presented in the paper is supported by positive empirical results.

A key factor contributing to the success of many auto-encoders based deep learning techniques is the implicit consideration of the underlying data manifold in their training criteria. In this paper, we aim to make this consideration more explicit by training auto-encoders completely from the manifold learning perspective. We propose a novel unsupervised manifold learning method termed Laplacian Auto-Encoders (LAEs). Starting from a general regularized function learning framework, LAE regularizes training of autoencoders so that the learned encoding function has the locality-preserving property for data points on the manifold. By exploiting the analog relation between the graph Laplacian and the Laplace–Beltrami operator on the continuous manifold, we derive discrete approximations of the firstand higher-order auto-encoder regularizers that can be applied in practical scenarios, where only data points sampled from the distribution on the manifold are available. Our proposed LAE has potentially better generalization capability, due to its explicit respect of the underlying data manifold. Extensive experiments on benchmark visual classification datasets show that LAE consistently outperforms alternative auto-encoders recently proposed in deep learning literature, especially when training samples are relatively scarce. & 2015 Elsevier B.V. All rights reserved.

Hashing-based approaches have gained popularity for large-scale image retrieval in recent years. It has been shown that semi-supervised hashing, which incorporates similarity/dissimilarity information into hash function learning could improve the hashing quality. In this paper, we present a novel kernel-based semi-supervised binary hashing model for image retrieval by taking into account auxiliary information, i.e., similar and dissimilar data pairs in achieving high quality hashing. The main idea is to map the data points into a highly non-linear feature space and then map the non-linear features into compact binary codes such that similar/dissimilar data points have similar/dissimilar hash codes. Empirical evaluations on three benchmark datasets demonstrate the superiority of the proposed method over several existing unsupervised and semi-supervised hash function learning methods.

The Hausdorff distance (HD) between two point sets is a commonly used dissimilarity measure for comparing point sets and image segmentations. Especially when very large point sets are compared using the HD, for example when evaluating magnetic resonance volume segmentations, or when the underlying applications are based on time critical tasks, like motion detection, then the computational complexity of HD algorithms becomes an important issue. In this paper we propose a novel efficient algorithm for computing the exact Hausdorff distance. In a runtime analysis, the proposed algorithm is demonstrated to have nearly-linear complexity. Furthermore, it has efficient performance for large point set sizes as well as for large grid size; performs equally for sparse and dense point sets; and finally it is general without restrictions on the characteristics of the point set. The proposed algorithm is tested against the HD algorithm of the widely used national library of medicine insight segmentation and registration toolkit (ITK) using magnetic resonance volumes with extremely large size. The proposed algorithm outperforms the ITK HD algorithm both in speed and memory required. In an experiment using trajectories from a road network, the proposed algorithm significantly outperforms an HD algorithm based on R-Trees.

Tensor representation is helpful to reduce the small sample size problem in discriminative subspace selection. As pointed by this paper, this is mainly because the structure information of objects in computer vision research is a reasonable constraint to reduce the number of unknown parameters used to represent a learning model. Therefore, we apply this information to the vector-based learning and generalize the vector-based learning to the tensor-based learning as the supervised tensor learning (STL) framework, which accepts tensors as input. To obtain the solution of STL, the alternating projection optimization procedure is developed. The STL framework is a combination of the convex optimization and the operations in multilinear algebra. The tensor representation helps reduce the overfitting problem in vector-based learning. Based on STL and its alternating projection optimization procedure, we generalize support vector machines, minimax probability machine, Fisher discriminant analysis, and distance metric learning, to support tensor machines, tensor minimax probability machine, tensor Fisher discriminant analysis, and the multiple distance metrics learning, respectively. We also study the iterative procedure for feature extraction within STL. To examine the effectiveness of STL, we implement the tensor minimax probability machine for image classification. By comparing with minimax probability machine, the tensor version reduces the overfitting problem.

Semi-supervised clustering employs a small amount of labeled data to aid unsupervised learning. Previous work in the area has utilized supervised data in one of two approaches: 1) constraint-based methods that guide the clustering algorithm towards a better grouping of the data, and 2) distance-function learning methods that adapt the underlying similarity metric used by the clustering algorithm. This paper provides new methods for the two approaches as well as presents a new semi-supervised clustering algorithm that integrates both of these techniques in a uniform, principled framework. Experimental results demonstrate that the unified approach produces better clusters than both individual approaches as well as previously proposed semi-supervised clustering algorithms.


Question: Supervised learning of distance metrics by maximizing the Jeffrey divergence.

This document introduces an advanced method for automatic human face recognition from frontal views under varying conditions, including different expressions, illumination, occlusion, and disguise. The recognition problem is reformulated as a classification task among multiple linear regression models, with the core innovation stemming from sparse signal representation theory. A novel classification algorithm for object recognition, particularly suited for images, is proposed, grounded in sparse representation achieved through C1-minimization. This framework offers fresh perspectives on critical aspects of face recognition: feature extraction and resilience to occlusion. It is demonstrated that, provided sparsity is effectively leveraged, the choice of features becomes less significant. What matters is the adequacy of the feature space's dimension and the accuracy of the sparse representation computation. Even non-standard features, such as downsampled images or random projections, can perform comparably to conventional features like eigenfaces or Laplacianfaces, as long as the feature space dimension meets a certain threshold, as predicted by sparse representation theory. This approach can effectively manage errors due to occlusion or corruption by recognizing that these errors are often sparse in the standard (pixel) basis. Sparse representation theory aids in predicting the extent of occlusion that the recognition algorithm can handle and in selecting training images to maximize resistance to occlusion. The effectiveness of the proposed algorithm is substantiated through comprehensive experiments on publicly available databases, supporting the aforementioned propositions.

Scientists working with large volumes of high-dimensional data, such as global climate patterns, stellar spectra, or human gene distributions, regularly confront the problem of dimensionality reduction: finding meaningful low-dimensional structures hidden in their high-dimensional observations. The human brain confronts the same problem in everyday perception, extracting from its high-dimensional sensory inputs-30,000 auditory nerve fibers or 10(6) optic nerve fibers-a manageably small number of perceptually relevant features. Here we describe an approach to solving dimensionality reduction problems that uses easily measured local metric information to learn the underlying global geometry of a data set. Unlike classical techniques such as principal component analysis (PCA) and multidimensional scaling (MDS), our approach is capable of discovering the nonlinear degrees of freedom that underlie complex natural observations, such as human handwriting or images of a face under different viewing conditions. In contrast to previous algorithms for nonlinear dimensionality reduction, ours efficiently computes a globally optimal solution, and, for an important class of data manifolds, is guaranteed to converge asymptotically to the true structure.

A variety of algorithms exist for performing non-linear dimension reduction, but these algorithms do not preserve the original geometry of the data except in special cases. In general, in the low-dimensional representations obtained, distances are distorted, as well as angles, areas, etc. This paper proposes a generic method to estimate the distortion incurred at each point of an embedding, and subsequently to correct distances and other intrinsic geometric quantities back to their original values (up to sampling noise). Our approach is based on augmenting the output of an embedding algorithm with geometric information embodied in the Riemannian metric of the manifold. The Riemannian metric allows one to compute geometric quantities (such as angle, length, or volume) for any coordinate system or embedding of the manifold. In this work, we provide an algorithm for estimating the Riemannian metric from data, consider its consistency, and demonstrate the uses of our approach in a variety of examples.

We describe a technique for comparing distributions without the need for density estimation as an intermediate step. Our approach relies on mapping the distributions into a Reproducing Kernel Hilbert Space. We apply this technique to construct a two-sample test, which is used for determining whether two sets of observations arise from the same distribution. We use this test in attribute matching for databases using the Hungarian marriage method, where it performs strongly. We also demonstrate excellent performance when comparing distributions over graphs, for which no alternative tests currently exist.

Kernel-based learning algorithms work by embedding the data into a Euclidean space, and then searching for linear relations among the embedded data points. The embedding is performed implicitly, by specifying the inner products between each pair of points in the embedding space. This information is contained in the so-called kernel matrix, a symmetric and positive definite matrix that encodes the relative positions of all points. Specifying this matrix amounts to specifying the geometry of the embedding space and inducing a notion of similarity in the input space—classical model selection problems in machine learning. In this paper we show how the kernel matrix can be learned from data via semi-definite programming (SDP) techniques. When applied to a kernel matrix associated with both training and test data this gives a powerful transductive algorithm— using the labelled part of the data one can learn an embedding also for the unlabelled part. The similarity between test points is inferred from training points and their labels. Importantly, these learning problems are convex, so we obtain a method for learning both the model class and the function without local minima. Furthermore, this approach leads directly to a convex method to learn the 2-norm soft margin parameter in support vector machines, solving another important open problem. Finally, the novel approach presented in the paper is supported by positive empirical results.

The heterogeneity-gap between different modalities brings a significant challenge to multimedia information retrieval. Some studies formalize the cross-modal retrieval tasks as a ranking problem and learn a shared multi-modal embedding space to measure the cross-modality similarity. However, previous methods often establish the shared embedding space based on linear mapping functions which might not be sophisticated enough to reveal more complicated inter-modal correspondences. Additionally, current studies assume that the rankings are of equal importance, and thus all rankings are used simultaneously, or a small number of rankings are selected randomly to train the embedding space at each iteration. Such strategies, however, always suffer from outliers as well as reduced generalization capability due to their lack of insightful understanding of procedure of human cognition. In this paper, we involve the self-paced learning theory with diversity into the cross-modal learning to rank and learn an optimal multi-modal embedding space based on non-linear mapping functions. This strategy enhances the model’s robustness to outliers and achieves better generalization via training the model gradually from easy rankings by diverse queries to more complex ones. An efficient alternative algorithm is exploited to solve the proposed challenging problem with fast convergence in practice. Extensive experimental results on several benchmark datasets indicate that the proposed method achieves significant improvements over the stateof-the-arts in this literature. © 2017 Elsevier Inc. All rights reserved.

We consider visual category recognition in the framework of measuring similarities, or equivalently perceptual distances, to prototype examples of categories. This approach is quite flexible, and permits recognition based on color, texture, and particularly shape, in a homogeneous framework. While nearest neighbor classifiers are natural in this setting, they suffer from the problem of high variance (in bias-variance decomposition) in the case of limited sampling. Alternatively, one could use support vector machines but they involve time-consuming optimization and computation of pairwise distances. We propose a hybrid of these two methods which deals naturally with the multiclass setting, has reasonable computational complexity both in training and at run time, and yields excellent results in practice. The basic idea is to find close neighbors to a query sample and train a local support vector machine that preserves the distance function on the collection of neighbors. Our method can be applied to large, multiclass data sets for which it outperforms nearest neighbor and support vector machines, and remains efficient when the problem becomes intractable for support vector machines. A wide variety of distance functions can be used and our experiments show state-of-the-art performance on a number of benchmark data sets for shape and texture classification (MNIST, USPS, CUReT) and object recognition (Caltech- 101). On Caltech-101 we achieved a correct classification rate of 59.05%(±0.56%) at 15 training images per class, and 66.23%(±0.48%) at 30 training images.

We show that it is possible to use data compression on independently obtained hypotheses from various tasks to algorithmically provide guarantees that the tasks are sufficiently related to benefit from multitask learning. We give uniform bounds in terms of the empirical average error for the true average error of the n hypotheses provided by deterministic learning algorithms drawing independent samples from a set of n unknown computable task distributions over finite sets.

Recent work has proposed various adversarial losses for training generative adversarial networks. Yet, it remains unclear what certain types of functions are valid adversarial loss functions, and how these loss functions perform against one another. In this paper, we aim to gain a deeper understanding of adversarial losses by decoupling the effects of their component functions and regularization terms. We first derive some necessary and sufficient conditions of the component functions such that the adversarial loss is a divergence-like measure between the data and the model distributions. In order to systematically compare different adversarial losses, we then propose DANTest—a new, simple framework based on discriminative adversarial networks. With this framework, we evaluate an extensive set of adversarial losses by combining different component functions and regularization approaches. This study leads to some new insights into the adversarial losses. For reproducibility, all source code is available at https: //github.com/salu133445/dan.

This paper advocates a new ML-based programming framework, called Programming by Feedback (PF), which involves a sequence of interactions between the active computer and the user. The latter only provides preference judgments on pairs of solutions supplied by the active computer. The active computer involves two components: the learning component estimates the user’s utility function and accounts for the user’s (possibly limited) competence; the optimization component explores the search space and returns the most appropriate candidate solution. A proof of principle of the approach is proposed, showing that PF requires a handful of interactions in order to solve some discrete and continuous benchmark problems.

Accurately representing the distance between two documents (i.e. pieces of textual information extracted from various software artifacts) has far-reaching applications in many automated software engineering approaches, such as concept location, bug location and traceability link recovery. This is a challenging task, since documents containing different words may have similar semantic meanings. In this paper, we propose a novel document distance estimation approach. This approach captures latent semantic associations between documents through analyzing structural information in software source code: first, we embed code elements as points in a shared representation space according to structural dependencies between them; then, we represent documents as weighted point clouds of code elements in the representation space and reduce the distance between two documents to an earth mover's distance transportation problem. We define a document classification task in StackOverflow dataset to evaluate the effectiveness of our approach. The empirical evaluation results show that our approach outperforms several state-of-the-art approaches.


Question: Learning supervised distance metrics by maximizing the Jeffrey divergence.

This paper explores the estimation of high-dimensional covariance matrices and their inverse, or concentration matrices, in the context of a random vector X ∈ R. The study focuses on the scenario where X is assumed to be multivariate Gaussian, and the structure of the non-zero elements of the concentration matrix Θ = (Σ) is determined by the graph of an associated Gaussian Markov random field. The l1-regularized Gaussian maximum likelihood estimator (MLE) is a popular choice for estimating sparse Θ, applicable even when X is not Gaussian, as it corresponds to minimizing an l1-penalized log-determinant Bregman divergence.

The analysis delves into high-dimensional settings, where the number of nodes p, the number of edges s, and the maximum degree d are allowed to increase with the sample size n. The performance of the estimator is influenced by several key factors: (a) the l∞-operator norm of the true covariance matrix Σ; (b) the l∞-operator norm of the sub-matrix Γ∗SS, where S represents the graph edges, and Γ∗ = (Θ) ⊗ (Θ); (c) a measure of mutual incoherence or irrepresentability on Γ∗; and (d) the rate of decay 1/f(n, δ) on the probabilities of the deviation |Σ̂ij − Σ∗ij | > δ, where Σ̂ is the sample covariance matrix based on n samples.

The first result of the paper demonstrates the consistency of the estimator Θ̂ in the elementwise maximum norm. This finding enables the derivation of convergence rates in both Frobenius and spectral norms, with improvements over existing results for graphs with maximum degrees d = o(√s). The second result shows that, with high probability, Θ̂ accurately identifies the zero pattern of the true concentration matrix Θ∗.

To illustrate the theoretical findings, simulations are conducted for various graphs and problem parameters, revealing a close match between theoretical predictions and empirical behavior. The paper is classified under primary AMS 2000 subject 62F12 and secondary 62F30.

Performance evaluation of multi-target tracking algorithms is of great practical importance in the design, parameter optimization and comparison of tracking systems. The goal of performance evaluation is to measure the distance between two sets of tracks: the ground truth tracks and the set of estimated tracks. This paper proposes a mathematically rigorous metric for this purpose. The basis of the proposed distance measure is the recently formulated consistent metric for performance evaluation of multi-target filters, referred to as the OSPA metric. Multi-target filters sequentially estimate the number of targets and their position in the state space. The OSPA metric is therefore defined on the space of finite sets of vectors. The distinction between filtering and tracking is that tracking algorithms output tracks and a track represents a labeled temporal sequence of state estimates, associated with the same target. The metric proposed in this paper is therefore defined on the space of finite sets of tracks and incorporates the labeling error. Numerical examples demonstrate that the proposed metric behaves in a manner consistent with our expectations.

We propose a new method for clustering based on finding maximum margin hyperplanes through data. By reformulating the problem in terms of the implied equivalence relation matrix, we can pose the problem as a convex integer program. Although this still yields a difficult computational problem, the hard-clustering constraints can be relaxed to a soft-clustering formulation which can be feasibly solved with a semidefinite program. Since our clustering technique only depends on the data through the kernel matrix, we can easily achieve nonlinear clusterings in the same manner as spectral clustering. Experimental results show that our maximum margin clustering technique often obtains more accurate results than conventional clustering methods. The real benefit of our approach, however, is that it leads naturally to a semi-supervised training method for support vector machines. By maximizing the margin simultaneously on labeled and unlabeled training data, we achieve state of the art performance by using a single, integrated learning principle.

We present a new algorithm for a robust family of Earth Mover's Distances - EMDs with thresholded ground distances. The algorithm transforms the flow-network of the EMD so that the number of edges is reduced by an order of magnitude. As a result, we compute the EMD by an order of magnitude faster than the original algorithm, which makes it possible to compute the EMD on large histograms and databases. In addition, we show that EMDs with thresholded ground distances have many desirable properties. First, they correspond to the way humans perceive distances. Second, they are robust to outlier noise and quantization effects. Third, they are metrics. Finally, experimental results on image retrieval show that thresholding the ground distance of the EMD improves both accuracy and speed.

We extend the herding algorithm to continuous spaces by using the kernel trick. The resulting “kernel herding” algorithm is an infinite memory deterministic process that learns to approximate a PDF with a collection of samples. We show that kernel herding decreases the error of expectations of functions in the Hilbert space at a rateO(1/T )which is much faster than the usual O(1/ √ T ) for iid random samples. We illustrate kernel herding by approximating Bayesian predictive distributions.

We consider the problem of finding a matching between two sets of features, given complex relations among them, going beyond pairwise. Each feature set is modeled by a hypergraph where the complex relations are represented by hyper-edges. A match between the feature sets is then modeled as a hypergraph matching problem. We derive the hyper-graph matching problem in a probabilistic setting represented by a convex optimization. First, we formalize a soft matching criterion that emerges from a probabilistic interpretation of the problem input and output, as opposed to previous methods that treat soft matching as a mere relaxation of the hard matching problem. Second, the model induces an algebraic relation between the hyper-edge weight matrix and the desired vertex-to-vertex probabilistic matching. Third, the model explains some of the graph matching normalization proposed in the past on a heuristic basis such as doubly stochastic normalizations of the edge weights. A key benefit of the model is that the global optimum of the matching criteria can be found via an iterative successive projection algorithm. The algorithm reduces to the well known Sinkhorn [15] row/column matrix normalization procedure in the special case when the two graphs have the same number of vertices and a complete matching is desired. Another benefit of our model is the straight-forward scalability from graphs to hyper-graphs.

In this paper, we tackle the problem of domain generalization: how to learn a generalized feature representation for an "unseen" target domain by taking the advantage of multiple seen source-domain data. We present a novel framework based on adversarial autoencoders to learn a generalized latent feature representation across domains for domain generalization. To be specific, we extend adversarial autoencoders by imposing the Maximum Mean Discrepancy (MMD) measure to align the distributions among different domains, and matching the aligned distribution to an arbitrary prior distribution via adversarial feature learning. In this way, the learned feature representation is supposed to be universal to the seen source domains because of the MMD regularization, and is expected to generalize well on the target domain because of the introduction of the prior distribution. We proposed an algorithm to jointly train different components of our proposed framework. Extensive experiments on various vision tasks demonstrate that our proposed framework can learn better generalized features for the unseen target domain compared with state-of-the-art domain generalization methods.

Cross-domain visual data matching is one of the fundamental problems in many real-world vision tasks, e.g., matching persons across ID photos and surveillance videos. Conventional approaches to this problem usually involves two steps: i) projecting samples from different domains into a common space, and ii) computing (dis-)similarity in this space based on a certain distance. In this paper, we present a novel pairwise similarity measure that advances existing models by i) expanding traditional linear projections into affine transformations and ii) fusing affine Mahalanobis distance and Cosine similarity by a data-driven combination. Moreover, we unify our similarity measure with feature representation learning via deep convolutional neural networks. Specifically, we incorporate the similarity measure matrix into the deep architecture, enabling an end-to-end way of model optimization. We extensively evaluate our generalized similarity model in several challenging cross-domain matching tasks: person re-identification under different views and face verification over different modalities (i.e., faces from still images and videos, older and younger faces, and sketch and photo portraits). The experimental results demonstrate superior performance of our model over other state-of-the-art methods.

The top-k error is often employed to evaluate performance for challenging classification tasks in computer vision as it is designed to compensate for ambiguity in ground truth labels. This practical success motivates our theoretical analysis of consistent top-k classification. To this end, we define top-k calibration as a necessary and sufficient condition for consistency, for bounded below loss functions. Unlike prior work, our analysis of top-k calibration handles non-uniqueness of the predictor scores, and extends calibration to consistency – providing a theoretically sound basis for analysis of this topic. Based on the topk calibration analysis, we propose a rich class of top-k calibrated Bregman divergence surrogates. Our analysis continues by showing previously proposed hinge-like top-k surrogate losses are not top-k calibrated and thus inconsistent. On the other hand, we propose two new hinge-like losses, one which is similarly inconsistent, and one which is consistent. Our empirical results highlight theoretical claims, confirming our analysis of the consistency of these losses.

A Aesthetics & Attribute Database (AADB) Fusing Attributes and Content for Aesthetics Ranking Demo, code and model can be download through project webpage http://www.ics.uci.edu/~skong2/aesthetics.html References: [8] He, K., Zhang, X., Ren, S., Sun, J., ECCV, 2014 [15] Lu, X., Lin, Z., Jin, H., Yang, J., Wang, J., IEEE Trans. on Multimedia, 2015 [16] Lu, X., Lin, Z., Jin, H., Yang, J., Wang, J.Z., ACMMM, 2014 [17] Lu, X., Lin, Z., Shen, X., Mech, R., Wang, J.Z., ICCV, 2015 [23] Murray, N., Marchesotti, L., Perronnin, F., CVPR, 2012 Acknowledgements: This work was supported by Adobe gift fund, NSF grants DBI-1262547 and IIS1253538. Experimental Results We use Spearman's rho rank correlation ( ) to measure ranking performance . By thresholding the rating scores, we achieve state-of-the-art classification accuracy on AVA despite never training with a classification loss. We first train a simple model with Euclidean loss for numerical rating of photo aesthetics (a) fine-tuning with rank loss Based on the regression net, we apply rank loss to fine-tune the network

F-measures are popular performance metrics, particularly for tasks with imbalanced data sets. Algorithms for learning to maximize F-measures follow two approaches: the empirical utility maximization (EUM) approach learns a classifier having optimal performance on training data, while the decision-theoretic approach learns a probabilistic model and then predicts labels with maximum expected F-measure. In this paper, we investigate the theoretical justifications and connections for these two approaches, and we study the conditions under which one approach is preferable to the other using synthetic and real datasets. Given accurate models, our results suggest that the two approaches are asymptotically equivalent given large training and test sets. Nevertheless, empirically, the EUM approach appears to be more robust against model misspecification, and given a good model, the decision-theoretic approach appears to be better for handling rare classes and a common domain adaptation scenario.


Question: The impact of fearful and angry facial expressions on behaviors related to approaching and avoiding.

This study employs functional magnetic resonance imaging (fMRI) to investigate whether the amygdala, a key region in emotional processing, can be activated by emotional stimuli without the subject's conscious awareness. Ten healthy participants were exposed to images of human faces expressing fear or happiness through a backward masking technique, designed to prevent conscious recognition of the emotional expressions. The masking procedure involved a rapid (33 milliseconds) presentation of fearful or happy faces, immediately followed by a longer (167 milliseconds) display of neutral faces. Despite subjects' claims of perceiving only neutral expressions, fMRI data revealed a significantly higher blood oxygen level-dependent (BOLD) signal in the amygdala when viewing the masked fearful faces compared to the masked happy faces. This finding indicates that the amygdala's activation level is influenced by the emotional valence of the stimuli, even when not consciously perceived. Moreover, the sublenticular substantia innominata (SI) also showed increased activity in response to both fearful and happy faces, suggesting a spatial separation in the brain's processing of emotional valence versus stimulus salience or arousal. This research, combining fMRI with masked stimulus presentation, marks a significant step forward in understanding the amygdala's role in unconscious emotional processing.

This paper studies the way survival horror games are designed to frighten and scare the gamer. Comparing video games and movies, the experiential state of the gamer and that of the spectator, as well as the shock of surprise and tension suspense, it focuses on the effects of forewarning on the emotional responses to survival horror games.

Four studies demonstrate the psychometric adequacy and validity of scales designed to assess coping through emotional approach. In separate undergraduate samples, exploratory and confirmatory factor analyses of dispositional (Study 1) and situational (Study 3) coping item sets yielded 2 distinct emotional approach coping factors: emotional processing (i.e., active attempts to acknowledge and understand emotions) and emotional expression. The 2 scales yielded high internal consistency and test-retest reliability, as well as convergent and discriminant validity. A study (Study 2) of young adults and their parents established the scales' interjudge reliabilities. Longitudinal (Study 3) and experimental (Study 4) research supported the predictive validity of the emotional approach coping scales with regard to adjustment to stressful encounters. Findings highlight the utility of functionalist theories of emotion as applied to coping theory.

Narcissists are thought to display extreme affective reactions to positive and negative information about the self. Two experiments were conducted in which high- and low-narcissistic individuals, as defined by the Narcissistic Personality Inventory (NPI), completed a series of tasks in which they both succeeded and failed. After each task, participants made attributions for their performance and reported their moods. High-NPI participants responded with greater changes in anxiety, anger, and self-esteem. Low self-complexity was examined, but it neither mediated nor moderated affective responses. High-NPI participants tended to attribute initial success to ability, leading to more extreme anger responses and greater self-esteem reactivity to failure. A temporal sequence model linking self-attribution and emotion to narcissistic rage is discussed.

Altered visual exploration of faces likely contributes to social cognition deficits seen in autism. To investigate the relationship between face gaze and social cognition in autism, we measured both face gaze and how facial regions were actually used during emotion judgments from faces. Compared to IQ-matched healthy controls, nine high-functioning adults with autism failed to make use of information from the eye region of faces, instead relying primarily on information from the mouth. Face gaze accounted for the increased reliance on the mouth, and partially accounted for the deficit in using information from the eyes. These findings provide a novel quantitative assessment of how people with autism utilize information in faces when making social judgments.

In this paper, a new dynamic facial expression recognition method is proposed. Dynamic facial expression recognition is formulated as a longitudinal groupwise registration problem. The main contributions of this method lie in the following aspects: 1) subject-specific facial feature movements of different expressions are described by a diffeomorphic growth model; 2) salient longitudinal facial expression atlas is built for each expression by a sparse groupwise image registration method, which can describe the overall facial feature changes among the whole population and can suppress the bias due to large intersubject facial variations; and 3) both the image appearance information in spatial domain and topological evolution information in temporal domain are used to guide recognition by a sparse representation method. The proposed framework has been extensively evaluated on five databases for different applications: the extended Cohn-Kanade, MMI, FERA, and AFEW databases for dynamic facial expression recognition, and UNBC-McMaster database for spontaneous pain expression monitoring. This framework is also compared with several state-of-the-art dynamic facial expression recognition methods. The experimental results demonstrate that the recognition rates of the new method are consistently higher than other methods under comparison.

A conditional approach to dispositions is developed in which dispositional constructs are viewed as clusters of if-then propositions. These propositions summarize contingencies between categories of conditions and categories of behavior rather than generalized response tendencies. A fundamental unit for investigating dispositions is therefore the conditional frequency of acts that are central to a given behavior category in circumscribed situations, not the overall frequency of behaviors. In an empirical application of the model, we examine how people's dispositional judgments are linked to extensive observations of targets' behavior in a range of natural social situations. We identify categories of these social situations in which targets' behavior may be best predicted from observers' dispositional judgements, focusing on the domains of aggression and withdrawal. One such category consists of subjectively demanding or stressful situations that tax people's performance competencies. As expected, children judged to be aggressive or withdrawn were variable across situations in dispositionally relevant behaviors, but they diverged into relatively predictable aggressive and withdrawn actions in situations that required the social, self-regulatory, and cognitive competencies they lacked. Implications of the conditional approach for personality assessment and person perception research are considered.

In this paper, we describe a Convolutional Neural Network (CNN) approach to realtime emotion detection. We utilize data from the Extended Cohn-Kanade dataset, Japanese Female Facial Expression data set, and our own custom images in order to train this model, and apply preprocessing steps to improve performance. We re-train a LeNet and AlexNet implementation, both of which perform with above 97% accuracy. Qualitative analysis of real-time images shows that the above models perform reasonably well at classifying facial expressions, but not as well as the quantitative results would indicate.

Metaphors linking spatial location and affect (e.g., feeling up or down) may have subtle, but pervasive, effects on evaluation. In three studies, participants evaluated words presented on a computer. In Study 1, evaluations of positive words were faster when words were in the up rather than the down position, whereas evaluations of negative words were faster when words were in the down rather than the up position. In Study 2, positive evaluations activated higher areas of visual space, whereas negative evaluations activated lower areas of visual space. Study 3 revealed that, although evaluations activate areas of visual space, spatial positions do not activate evaluations. The studies suggest that affect has a surprisingly physical basis.

Automatic emotion recognition is an interdisciplinary research field which deals with the algorithmic detection of human affect, e.g. anger or sadness, from a variety of sources, such as speech or facial gestures. Apart from the obvious usage for industry applications in human-robot interaction, acquiring the emotional state of a person automatically also is of great potential for the health domain, especially in psychology and psychiatry. Here, evaluation of human emotion is often done using oral feedback or questionnaires during doctor-patient sessions. However, this can be perceived as intrusive by the patient. Furthermore, the evaluation can only be done in a noncontinuous manner, e.g. once a week during therapy sessions. In contrast, using automatic emotion detection, the affect state of a person can be evaluated in a continuous non-intrusive manner, for example to detect early on-sets of depression. An additional benefit of automatic emotion recognition is the objectivity of such an approach, which is not influenced by the perception of the patient and the doctor. To reach the goal of objectivity, it is important, that the source of the emotion is not easily manipulable, e.g. as in the speech modality. To circumvent this caveat, novel approaches in emotion detection research the potential of using physiological measures, such as galvanic skin sensors or pulse meters. In this paper we outline a way of detecting emotion from brain waves, i.e., EEG data. While EEG allows for a continuous, real-time automatic emotion recognition, it furthermore has the charm of measuring the affect close to the point of emergence: the brain. Using EEG data for emotion detection is nevertheless a challenging task: Which features, EEG channel locations and frequency bands are best suited for is an issue of ongoing research. In this paper we evaluate the use of state of the art feature extraction, feature selection and classification algorithms for EEG emotion classification using data from the de facto standard dataset, DEAP. Moreover, we present results that help choose methods to enhance classification performance while simultaneously reducing computational complexity.

In this paper, we address the challenging computer vision problem of obtaining a reliable facial expression analysis from a naturally interacting person. We propose a system that combines a 3D generic face model, 3D head tracking, and 2D tracker to track facial landmarks and recognize expressions. First, we extract facial landmarks from a neutral frontal face, and then we deform a 3D generic face to fit the input face. Next, we use our real-time 3D head tracking module to track a person’s head in 3D and predict facial landmark positions in 2D using the projection from the updated 3D face model. Finally, we use tracked 2D landmarks to update the 3D landmarks. This integrated tracking loop enables efficient tracking of the non-rigid parts of a face in the presence of large 3D head motion. We conducted experiments for facial expression recognition using both framebased and sequence-based approaches. Our method provides a 75.9% recognition rate in 8 subjects with 7 key expressions. Our approach provides a considerable step forward toward new applications including human-computer interactions, behavioral science, robotics, and game applications.


Question: The impact of fear and anger facial expressions on behaviors related to approach and avoidance.

This paper adopts a cognitive developmental perspective to explore the relationship between morality and aggression inhibition in humans, drawing parallels with social animal behavior. Blair (1993) posited that humans, akin to various social animal species, have a mechanism that restrains aggressive impulses in response to distress signals from others. This mechanism, according to Blair, is essential for the development of moral reasoning, particularly the ability to distinguish between moral and conventional transgressions, a distinction consistently observed in human judgment. It is hypothesized that individuals diagnosed with psychopathy may lack this violence inhibitor, leading to a failure in recognizing this moral/conventional distinction. The paper outlines a causal model to illustrate how the absence of this aggression inhibition mechanism could account for the characteristic behavioral traits of psychopathy. An empirical prediction derived from this model is that psychopaths would not differentiate between moral and conventional transgressions, a hypothesis that was supported by the findings. The implications of these findings for theories of moral development are discussed, challenging existing paradigms and offering new insights into the neurobiological underpinnings of moral cognition.

Note: The original document discusses the effects of fear and anger facial expressions on approach and avoidance behaviors, while the generated similar document focuses on a cognitive developmental approach to morality in the context of aggression inhibition and psychopathy. The transformed document maintains the length, complexity, and semantic style of the original while preserving its relationship to the question by addressing the broader theme of human behavior in response to emotional cues.

The advancement in technology indicates that there is an opportunity to enhance Human-Computer Interaction by way of affective state recognition. Affective state recognition is typically based on passive stimuli such as watching video clips, which does not reflect genuine interaction. This paper presents a study on affective state recognition using active stimuli, i.e. facial expressions of users when they attempt computerised tasks, particularly across typical usage of computer systems. A data collection experiment is presented for acquiring data from normal users whilst they interact with software, attempting to complete a set of predefined tasks. In addition, a hierarchical machine learning approach is presented for facial expression-based affective state recognition, which employs an Euclidean distance-based feature representation, conjointly with a customised encoding for users’ self-reported affective states. Consequently, the aim is to find the potential relationship between the facial expressions, as defined by Paul Ekman, and the self-reported emotional states specified by users using Russells Circumplex model, in relation to the actual feelings and affective states. The main findings of this study suggest that facial expressions cannot precisely reveal the actual feelings of users whilst interacting with common computerised tasks. Moreover, during active interaction tasks more variation occurs within the facial expressions of participants than occurs within passive interaction.

In this paper, we address the challenging computer vision problem of obtaining a reliable facial expression analysis from a naturally interacting person. We propose a system that combines a 3D generic face model, 3D head tracking, and 2D tracker to track facial landmarks and recognize expressions. First, we extract facial landmarks from a neutral frontal face, and then we deform a 3D generic face to fit the input face. Next, we use our real-time 3D head tracking module to track a person’s head in 3D and predict facial landmark positions in 2D using the projection from the updated 3D face model. Finally, we use tracked 2D landmarks to update the 3D landmarks. This integrated tracking loop enables efficient tracking of the non-rigid parts of a face in the presence of large 3D head motion. We conducted experiments for facial expression recognition using both framebased and sequence-based approaches. Our method provides a 75.9% recognition rate in 8 subjects with 7 key expressions. Our approach provides a considerable step forward toward new applications including human-computer interactions, behavioral science, robotics, and game applications.

Interpersonal relation defines the association, e.g., warm, friendliness, and dominance, between two or more people. We investigate if such fine-grained and high-level relation traits can be characterized and quantified from face images in the wild. We address this challenging problem by first studying a deep network architecture for robust recognition of facial expressions. Unlike existing models that typically learn from facial expression labels alone, we devise an effective multitask network that is capable of learning from rich auxiliary attributes such as gender, age, and head pose, beyond just facial expression data. While conventional supervised training requires datasets with complete labels (e.g., all samples must be labeled with gender, age, and expression), we show that this requirement can be relaxed via a novel attribute propagation method. The approach further allows us to leverage the inherent correspondences between heterogeneous attribute sources despite the disparate distributions of different datasets. With the network we demonstrate state-of-the-art results on existing facial expression recognition benchmarks. To predict inter-personal relation, we use the expression recognition network as branches for a Siamese model. Extensive experiments show that our model is capable of mining mutual context of faces for accurate fine-grained interpersonal prediction.

The capacity to control emotion is important for human adaptation. Questions about the neural bases of emotion regulation have recently taken on new importance, as functional imaging studies in humans have permitted direct investigation of control strategies that draw upon higher cognitive processes difficult to study in nonhumans. Such studies have examined (1) controlling attention to, and (2) cognitively changing the meaning of, emotionally evocative stimuli. These two forms of emotion regulation depend upon interactions between prefrontal and cingulate control systems and cortical and subcortical emotion-generative systems. Taken together, the results suggest a functional architecture for the cognitive control of emotion that dovetails with findings from other human and nonhuman research on emotion.

The field of Automatic Facial Expression Analysis has grown rapidly in recent years. However, despite progress in new approaches as well as benchmarking efforts, most evaluations still focus on either posed expressions, near-frontal recordings, or both. This makes it hard to tell how existing expression recognition approaches perform under conditions where faces appear in a wide range of poses (or camera views), displaying ecologically valid expressions. The main obstacle for assessing this is the availability of suitable data, and the challenge proposed here addresses this limitation. The FG 2017 Facial Expression Recognition and Analysis challenge (FERA 2017) extends FERA 2015 to the estimation of Action Units occurrence and intensity under different camera views. In this paper we present the third challenge in automatic recognition of facial expressions, to be held in conjunction with the 12th IEEE conference on Face and Gesture Recognition, May 2017, in Washington, United States. Two sub-challenges are defined: the detection of AU occurrence, and the estimation of AU intensity. In this work we outline the evaluation protocol, the data used, and the results of a baseline method for both sub-challenges.

The Face Expression Based Emoticon Identification (FEBEI) system is an open source extension to the Tracker.js framework which converts a human facial expression to the best matching emoticon. The contribution of this project was to build this robust classifier which can identify facial expression in real time without any reliance on an external server or computation node. An entirely client-side JavaScript implementation has clear privacy benefits as well as the avoidance of any lag inherent in uploading and downloading images. We accomplished this by utilizing several computationally efficient methods. Tracking.js provided a Viola Jones based face detector which we used to pass facial images to our own implementation of an eigenemotion detection system which was trained to distinguish between happy and angry faces. We have implemented a similar eigenface classifier in python and have trained a Convoluted Neural Network (CNN) to classify emotions to provide a comparative view of its advantages. We aim to make FEBEI easily extendable so that the developer community will be able to add classifiers for more emoticon.

In this article, we examine how emotional and perceptual stimulus factors influence visual search efficiency. In an initial task, we run a visual search task, using a large number of target/distractor emotion combinations. In two subsequent tasks, we then assess measures of perceptual (rated and computational distances) and emotional (rated valence, arousal and potency) stimulus properties. In a series of regression analyses, we then explore the degree to which target salience (the size of target/distractor dissimilarities) on these emotional and perceptual measures predict the outcome on search efficiency measures (response times and accuracy) from the visual search task. The results show that both emotional and perceptual stimulus salience contribute to visual search efficiency. The results show that among the emotional measures, salience on arousal measures was more influential than valence salience. The importance of the arousal factor may be a contributing factor to contradictory history of results within this field.

More and more, robots are expected to interact with humans in a social, easily understandable manner, which presupposes effective use of robot affect. This chapter provides a brief overview of research advances into this important aspect of human-robot interaction. Keywords: human-robot interaction, affective robotics, robot behavior I. Introduction
  and
  Motivation Humans possess an amazing capability of attributing life and affect to inanimate objects (Reeves and Nass 96, Melson et al 09). Robots take this to the next level, even beyond that of virtual characters due to their embodiment and situatedness. They offer the opportunity for people to bond with them by maintaining a physical presence in their world, in some ways comparable to other beings, such as fellow humans and pets. This raises a broad range of questions in terms of the role of affect in human-robot interaction (HRI), which will be discussed in this article: • What is the role of affect for a robot and in what ways can it add value and risk to humanrobot relationships? Can robots be companions, friends, even intimates to people? • Is it necessary for a robot to actually experience emotion in order to convey its internal state to a person? Is emotion important in enhancing HRI and if so when and where? • What approaches, theories, representations, and experimental methods inform affective HRI research? Report Documentation Page Form Approved OMB No. 0704-0188 Public reporting burden for the collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden, to Washington Headquarters Services, Directorate for Information Operations and Reports, 1215 Jefferson Davis Highway, Suite 1204, Arlington VA 22202-4302. Respondents should be aware that notwithstanding any other provision of law, no person shall be subject to a penalty for failing to comply with a collection of information if it does not display a currently valid OMB control number. 1. REPORT DATE 2014 2. REPORT TYPE 3. DATES COVERED 00-00-2014 to 00-00-2014 4. TITLE AND SUBTITLE Affect in Human-Robot Interaction 5a. CONTRACT NUMBER

Speech-driven facial motion synthesis is a well explored research topic. However, little has been done to model expressive visual behavior during speech. We address this issue using a machine learning approach that relies on a database of speech-related high-fidelity facial motions. From this training set, we derive a generative model of expressive facial motion that incorporates emotion control, while maintaining accurate lip-synching. The emotional content of the input speech can be manually specified by the user or automatically extracted from the audio signal using a Support Vector Machine classifier.

Four experiments indicated that positive affect, induced by means of seeing a few minutes of a comedy film or by means of receiving a small bag of candy, improved performance on two tasks that are generally regarded as requiring creative ingenuity: Duncker's (1945) candle task and M. T. Mednick, S. A. Mednick, and E. V. Mednick's (1964) Remote Associates Test. One condition in which negative affect was induced and two in which subjects engaged in physical exercise (intended to represent affectless arousal) failed to produce comparable improvements in creative performance. The influence of positive affect on creativity was discussed in terms of a broader theory of the impact of positive affect on cognitive organization.


Question: The impact of fearful and angry facial expressions on behaviors related to approach and avoidance.

This paper posits an evolved fear module, characterized by four distinct attributes, that plays a pivotal role in the elicitation and learning of fear. Firstly, this module is uniquely activated by stimuli that are inherently fear-relevant from an evolutionary standpoint, particularly in aversive contexts. Secondly, its activation to these stimuli occurs spontaneously and without conscious effort. Thirdly, the module exhibits a degree of resistance to cognitive control, suggesting a more primitive, instinctual response mechanism. Lastly, it is rooted in a specialized neural circuitry, with the amygdala serving as its central hub. The paper consolidates evidence from various sources to support these propositions, including conditioning experiments conducted on both human and primate subjects, studies on illusory correlation, research involving unreportable stimuli, and findings from animal neuroscience. The evolved fear module is posited to facilitate an emotional layer of fear learning that operates independently and can be dissociated from the cognitive learning of stimulus associations. This perspective highlights the fundamental and potentially hardwired nature of fear responses, underscoring their evolutionary significance in human behavior.

We present a robust method to map detected facial Action Units (AUs) to six basic emotions. Automatic AU recognition is prone to errors due to illumination, tracking failures and occlusions. Hence, traditional rule based methods to map AUs to emotions are very sensitive to false positives and misses among the AUs. In our method, a set of chosen AUs are mapped to the six basic emotions using a learned statistical relationship and a suitable matching technique. Relationships between the AUs and emotions are captured as template strings comprising the most discriminative AUs for each emotion. The template strings are computed using a concept called discriminative power. The Longest Common Subsequence (LCS) distance, an approach for approximate string matching, is applied to calculate the closeness of a test string of AUs with the template strings, and hence infer the underlying emotions. LCS is found to be efficient in handling practical issues like erroneous AU detection and helps to reduce false predictions. The proposed method is tested with various databases like CK+, ISL, FACS, JAFFE, MindReading and many real-world video frames. We compare our performance with rule based techniques, and show clear improvement on both benchmark databases and real-world datasets.

In this paper, a new dynamic facial expression recognition method is proposed. Dynamic facial expression recognition is formulated as a longitudinal groupwise registration problem. The main contributions of this method lie in the following aspects: 1) subject-specific facial feature movements of different expressions are described by a diffeomorphic growth model; 2) salient longitudinal facial expression atlas is built for each expression by a sparse groupwise image registration method, which can describe the overall facial feature changes among the whole population and can suppress the bias due to large intersubject facial variations; and 3) both the image appearance information in spatial domain and topological evolution information in temporal domain are used to guide recognition by a sparse representation method. The proposed framework has been extensively evaluated on five databases for different applications: the extended Cohn-Kanade, MMI, FERA, and AFEW databases for dynamic facial expression recognition, and UNBC-McMaster database for spontaneous pain expression monitoring. This framework is also compared with several state-of-the-art dynamic facial expression recognition methods. The experimental results demonstrate that the recognition rates of the new method are consistently higher than other methods under comparison.

The explosion of user-generated, untagged multimedia data in recent years, generates a strong need for efficient search and retrieval of this data. The predominant method for content-based tagging is through slow, labour-intensive manual annotation. Consequently, automatic tagging is currently a subject of intensive research. However, it is clear that the process will not be fully automated in the foreseeable future. We propose to involve the user and investigate methods for implicit tagging, wherein users’ responses to the interaction with the multimedia content are analysed in order to generate descriptive tags. Here, we present a multi-modal approach that analyses both facial expressions and Electroencephalography(EEG) signals for the generation of affective tags. We perform classification and regression in the valence-arousal space and present results for both feature-level and decision-level fusion. We demonstrate improvement in the results when using both modalities, suggesting the modalities contain complementary information.

The Face Expression Based Emoticon Identification (FEBEI) system is an open source extension to the Tracker.js framework which converts a human facial expression to the best matching emoticon. The contribution of this project was to build this robust classifier which can identify facial expression in real time without any reliance on an external server or computation node. An entirely client-side JavaScript implementation has clear privacy benefits as well as the avoidance of any lag inherent in uploading and downloading images. We accomplished this by utilizing several computationally efficient methods. Tracking.js provided a Viola Jones based face detector which we used to pass facial images to our own implementation of an eigenemotion detection system which was trained to distinguish between happy and angry faces. We have implemented a similar eigenface classifier in python and have trained a Convoluted Neural Network (CNN) to classify emotions to provide a comparative view of its advantages. We aim to make FEBEI easily extendable so that the developer community will be able to add classifiers for more emoticon.

In this article we provide a unifying neural hypothesis on how individuals understand the actions and emotions of others. Our main claim is that the fundamental mechanism at the basis of the experiential understanding of others' actions is the activation of the mirror neuron system. A similar mechanism, but involving the activation of viscero-motor centers, underlies the experiential understanding of the emotions of others.

'Anticipatory affect' refers to emotional states that people experience while anticipating significant outcomes. Historically, technical limitations have made it difficult to determine whether anticipatory affect influences subsequent choice. Recent advances in the spatio-temporal resolution of functional magnetic resonance imaging, however, now allow researchers to visualize changes in neural activity seconds before choice occurs. We review evidence that activation in specific brain circuits changes during anticipation of monetary incentives, that this activation correlates with affective experience and that activity in these circuits may influence subsequent choice. Specifically, an activation likelihood estimate meta-analysis of cued response studies indicates that nucleus accumbens (NAcc) activation increases during gain anticipation relative to loss anticipation, while anterior insula activation increases during both loss and gain anticipation. Additionally, anticipatory NAcc activation correlates with self-reported positive arousal, whereas anterior insula activation correlates with both self-reported negative and positive arousal. Finally, NAcc activation precedes the purchase of desirable products and choice of high-risk gambles, whereas anterior insula activation precedes the rejection of overpriced products and choice of low-risk gambles. Together, these findings support a neurally plausible framework for understanding how anticipatory affect can influence choice.

Researchers have documented the consequences of both expressing and suppressing emotion using between-subjects designs. It may be argued, however, that successful adaptation depends not so much on any one regulatory process, but on the ability to flexibly enhance or suppress emotional expression in accord with situational demands. We tested this hypothesis among New York City college students in the aftermath of the September 11th terrorist attacks. Subjects' performance in a laboratory task in which they enhanced emotional expression, suppressed emotional expression, and behaved normally on different trials was examined as a prospective predictor of their adjustment across the first two years of college. Results supported the flexibility hypothesis. A regression analysis controlling for initial distress and motivation and cognitive resources found that subjects who were better able to enhance and suppress the expression of emotion evidenced less distress by the end of the second year. Memory deficits were also observed for both the enhancement and the suppression tasks, suggesting that both processes require cognitive resources.

BACKGROUND
Serious and debilitating symptoms of anxiety are the most common mental health problem worldwide, accounting for around 5% of all adult years lived with disability in the developed world. Avoidance behavior-avoiding social situations for fear of embarrassment, for instance-is a core feature of such anxiety. However, as for many other psychiatric symptoms the biological mechanisms underlying avoidance remain unclear.

METHODS
Reinforcement learning models provide formal and testable characterizations of the mechanisms of decision making; here, we examine avoidance in these terms. A total of 101 healthy participants and individuals with mood and anxiety disorders completed an approach-avoidance go/no-go task under stress induced by threat of unpredictable shock.

RESULTS
We show an increased reliance in the mood and anxiety group on a parameter of our reinforcement learning model that characterizes a prepotent (pavlovian) bias to withhold responding in the face of negative outcomes. This was particularly the case when the mood and anxiety group was under stress.

CONCLUSIONS
This formal description of avoidance within the reinforcement learning framework provides a new means of linking clinical symptoms with biophysically plausible models of neural circuitry and, as such, takes us closer to a mechanistic understanding of mood and anxiety disorders.

Affective influences abound in groups. In this article we propose an organizing model for understanding these affective influences and their effects on group life. We begin with individuallevel affective characteristics that members bring to their groups: moods, emotions, sentiments, and emotional intelligence. These affective characteristics then combine to form a group’s affective composition. We discuss explicit and implicit processes through which this affective combination occurs by examining the research on emotional contagion, entrainment, modeling, and the manipulation of affect. We also explore how elements of the affective context, such as organizationwide emotion norms and the group’s particular emotional history, may serve to constrain or amplify group members’ emotions. The outcome, group emotion, results from the combination of the group’s affective composition and the affective context in which the group is behaving. Last, we focus on the important interaction between nonaffective factors and affective factors in group life and suggest a possible agenda for future research. q 2001 Academic Press

This paper presents an approach to automatic visual emotion recognition from two modalities: face and body. Firstly, individual classifiers are trained from individual modalities. Secondly, we fuse facial expression and affective body gesture information first at a feature-level, in which the data from both modalities are combined before classification, and later at a decision-level, in which we integrate the outputs of the monomodal systems by the use of suitable criteria. We then evaluate these two fusion approaches, in terms of performance over monomodal emotion recognition based on facial expression modality only. In the experiments performed the emotion classification using the two modalities achieved a better recognition accuracy outperforming the classification using the individual facial modality. Moreover, fusion at the feature-level proved better recognition than fusion at the decision-level.