A Spurious Equilibria-free Learning Algorithm for the Blind Separation of Non-zoer Skewness Signals

Seungjin Choi, Ruey Wen Liu, Andrzej Cichocki

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)

Abstract

We present a new learning algorithm for the blind separation of independent source signals having non-zero skewness (the 3rd-order cumulant) (the source signals have non-symmetric probability distribution.), from their linear mixtures. It is shown that for a class of source signals whose probability distribution functions is not symmetric, a simple adaptive learning algorithm using quadratic function (f(x) = x2) is very efficient for blind source separation task. It is proved that all stable equilibria of the proposed learning algorithm are desirable solutions. Extensive computer simulation experiments confirmed the validity of the proposed algorithm.

Original languageEnglish
Pages (from-to)61-68
Number of pages8
JournalNeural Processing Letters
Volume7
Issue number2
DOIs
Publication statusPublished - 1998
Externally publishedYes

Keywords

  • Blind source separation
  • Higher-order statistics
  • Neural networks
  • Unsupervised learning

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