Extreme Entropy Machines: Robust information theoretic classification

TL;DR

This paper introduces Extreme Entropy Machines, a novel classification approach based on entropy measures, offering a robust and scalable alternative to traditional methods like SVMs and ELMs, with strong empirical performance.

Contribution

It proposes a new information theoretic classification model using quadratic Renyi's entropy and Cauchy-Schwarz Divergence, providing robustness and scalability.

Findings

Competitive accuracy with state-of-the-art classifiers

Effective on large and highly unbalanced datasets

Scales well to hundreds of thousands of samples

Abstract

Most of the existing classification methods are aimed at minimization of empirical risk (through some simple point-based error measured with loss function) with added regularization. We propose to approach this problem in a more information theoretic way by investigating applicability of entropy measures as a classification model objective function. We focus on quadratic Renyi's entropy and connected Cauchy-Schwarz Divergence which leads to the construction of Extreme Entropy Machines (EEM). The main contribution of this paper is proposing a model based on the information theoretic concepts which on the one hand shows new, entropic perspective on known linear classifiers and on the other leads to a construction of very robust method competetitive with the state of the art non-information theoretic ones (including Support Vector Machines and Extreme Learning Machines). Evaluation on numerous problems spanning from small, simple ones from UCI repository to the large (hundreads of thousands of samples) extremely unbalanced (up to 100:1 classes' ratios) datasets shows wide applicability of the EEM in real life problems and that it scales well.