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MISEP - Linear and Nonlinear ICA Based on Mutual Information

By Luis B. Almeida

Abstract

MISEP is a method for linear and nonlinear ICA, that is able to handle a large variety of situations. It is an extension of the well known INFOMAX method, in two directions: (1) handling of nonlinear mixtures, and (2) learning the nonlinearities to be used at the outputs. The method can therefore separate linear and nonlinear mixtures of components with a wide range of statistical distributions. This paper presents the basis of the MISEP method, as well as experimental results obtained with it. The results illustrate the applicability of the method to various situations, and show that, although the nonlinear blind separation problem is ill-posed, use of regularization allows the problem to be solved when the nonlinear mixture is relatively smooth

Topics: Statistical Models, Machine Learning, Artificial Intelligence
Publisher: Elsevier
Year: 2003
OAI identifier: oai:cogprints.org:2856
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