A new fixed-point algorithm for independent component analysis

Zhenwei Shi, Huanwen Tang, Yiyuan Tang

Research output: Contribution to journalArticle

30 Scopus citations

Abstract

A new fixed-point algorithm for independent component analysis (ICA) is presented that is able blindly to separate mixed signals with sub- and super-Gaussian source distributions. The new fixed-point algorithm maximizes the likelihood of the ICA model under the constraint of decorrelation and uses the method of Lee et al. (Neural Comput. 11(2) (1999) 417) to switch between sub- and super-Gaussian regimes. The new fixed-point algorithm maximizes the likelihood very fast and reliably. The validity of this algorithm is confirmed by the simulations and experimental results.

Original languageEnglish
Pages (from-to)467-473
Number of pages7
JournalNeurocomputing
Volume56
Issue number1-4
DOIs
StatePublished - Jan 2004

Keywords

  • Blind source separation
  • Fixed-point algorithm
  • Independent component analysis

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