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Minimax Mutual Information Approach for ICA of Complex-Valued Linear Mixtures

  • Jian-Wu Xu
  • Deniz Erdogmus
  • Yadunandana N. Rao
  • José Carlos Príncipe
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3195)

Abstract

Recently, the authors developed the Minimax Mutual Information algorithm for linear ICA of real-valued mixtures, which is based on a density estimate stemming from Jaynes’ maximum entropy principle. Since the entropy estimates result in an approximate upper bound for the actual mutual information of the separated outputs, minimizing this upper bound results in a robust performance and good generalization. In this paper, we extend the mentioned algorithm to complex-valued mixtures. Simulations with artificial data demonstrate that the proposed algorithm outperforms FastICA.

Keywords

Mutual Information Independent Component Analysis Independent Component Analysis Blind Source Separation Maximum Entropy Principle 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Jian-Wu Xu
    • 1
  • Deniz Erdogmus
    • 1
  • Yadunandana N. Rao
    • 1
  • José Carlos Príncipe
    • 1
  1. 1.CNEL, Electrical and Computer Engineering DepartmentUniversity of FloridaGainesvilleUSA

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