Renyi’s Entropy, Divergence and Their Nonparametric Estimators

Abstract

It is evident from Chapter 1 that Shannon’s entropy occupies a central role in information-theoretic studies. Yet, the concept of information is so rich that perhaps there is no single definition that will be able to quantify information properly. Moreover, from an engineering perspective, one must estimate entropy from data which is a nontrivial matter. In this book we concentrate on Alfred Renyi’s seminal work on information theory to derive a set of estimators to apply entropy and divergence as cost functions in adaptation and learning. Therefore, we are mainly interested in computationally simple, nonparametric estimators that are continuous and differentiable in terms of the samples to yield well-behaved gradient algorithms that can optimize adaptive system parameters. There are many factors that affect the determination of the optimum of the performance surface, such as gradient noise, learning rates, and misadjustment, therefore in these types of applications the entropy estimator’s bias and variance are not as critical as, for instance, in coding or rate distortion theories. Moreover in adaptation one is only interested in the extremum (maximum or minimum) of the cost, with creates independence from its actual values, because only relative assessments are necessary. Following our nonparametric goals, what matters most in learning is to develop cost functions or divergence measures that can be derived directly from data without further assumptions to capture as much structure as possible within the data’s probability density function (PDF).