A new information-theoretic based ICA algorithm for blind signal separation

Yiu Ming CHEUNG*, Lei Xu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

The typical information-theoretic approaches such as INFOMAX and MMI perform independent component analysis (ICA) by using a fixed nonlinearity function. Consequently, they can only separate either sub-Gaussian or super-Gaussian source signals, but not both. This article considers a flexible nonlinearity function that is a single polynomial term with the exponent learnable. The separation ability of this function is analysed, and a new ICA algorithm is proposed. The experiments have shown that this algorithm can successfully separate the mixture of sub-Gaussian and super-Gaussian sources.

Original languageEnglish
Pages (from-to)106-110
Number of pages5
JournalInternational Journal of Computers and Applications
Volume25
Issue number2
DOIs
Publication statusPublished - 2003

Scopus Subject Areas

  • Software
  • Hardware and Architecture
  • Computer Science Applications
  • Computer Graphics and Computer-Aided Design

User-Defined Keywords

  • Blind signal separation
  • Flexible nonlinearity function
  • Independent component analysis
  • Sub-Gaussian and super-Gaussian sources

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