Divergence estimation for machine learning and signal processing

Abstract

Approximating a divergence between two probability distributions from their samples is a fundamental challenge in the statistics, information theory, and machine learning communities, because a divergence estimator can be used for various purposes such as two-sample homogeneity testing, change-point detection, and class-balance estimation. Furthermore, an approximator of a divergence between the joint distribution and the product of marginals can be used for independence testing, which has a wide range of applications including feature selection and extraction, clustering, object matching, independent component analysis, and causality learning. In this talk, we review recent advances in direct divergence approximation that follow the general inference principle advocated by Vladimir Vapnik-one should not solve a more general problem as an intermediate step. More specifically, direct divergence approximation avoids separately estimating two probability distributions when approximating a divergence. We cover direct approximators of the Kullback-Leibler (KL) divergence, the Pearson (PE) divergence, the relative PE (rPE) divergence, and the L2-distance. Despite the overwhelming popularity of the KL divergence, we argue that the latter approximators are more useful in practice due to their computational efficiency, high numerical stability, and superior robustness against outliers.