Blind Source Separation by Entropy Rate Minimization

Abstract

By assuming latent sources are statistically independent, independent component analysis separates underlying sources from a given linear mixture. Since in many applications, latent sources are both non-Gaussian and have sample dependence, it is desirable to exploit both properties jointly. In this paper, we use mutual information rate to construct a general framework for analysis and derivation of algorithms that take both properties into account. We discuss two types of source models for entropy rate estimation-a Markovian and an invertible filter model-and give the general independent component analysis cost function, update rule, and performance analysis based on these. We also introduce four algorithms based on these two models, and show that their performance can approach the Cramer-Rao lower bound. In addition, we demonstrate that the algorithms with flexible models exhibit very desirable performance for “natural” data.