Information-theoretic approach to blind separation of sources in non-linear mixture

Howard Hua Yang, Shun Ichi Amari, Andrzej Cichocki

Research output: Contribution to journalArticlepeer-review

117 Citations (Scopus)

Abstract

The linear mixture model is assumed in most of the papers devoted to blind separation. A more realistic model for mixture should be non-linear. In this paper, a two-layer perceptron is used as a de-mixing system to separate sources in non-linear mixture. The learning algorithms for the de-mixing system are derived by two approaches: maximum entropy and minimum mutual information. The algorithms derived from the two approaches have a common structure. The new learning equations for the hidden layer are different from the learning equations for the output layer. The natural gradient descent method is applied in maximizing entropy and minimizing mutual information. The information (entropy or mutual information) back-propagation method is proposed to derive the learning equations for the hidden layer.

Original languageEnglish
Pages (from-to)291-300
Number of pages10
JournalSignal Processing
Volume64
Issue number3
DOIs
Publication statusPublished - 26 Feb 1998
Externally publishedYes

Keywords

  • Blind separation
  • Information back-propagation
  • Maximum entropy
  • Minimum mutual information
  • Non-linear mixture

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