A characteristic function based contrast function for blind extraction of statistically independent signals

Muhammad Tufail, Masahide Abe, Masayuki Kawamata

Research output: Contribution to journalArticlepeer-review

Abstract

In this paper, we propose to employ a characteristic function based non-Gaussianity measure as a one-unit contrast function for independent component analysis. This non-Gaussianity measure is a weighted distance between the characteristic function of a random variable and a Gaussian characteristic function at some adequately chosen sample points. Independent component analysis of an observed random vector is performed by optimizing the above mentioned contrast function (for different units) using a fixed-point algorithm. Moreover, in order to obtain a better separation performance, we employ a mechanism to choose appropriate sample points from an initially selected sample vector. Finally, some computer simulations are presented to demonstrate the validity and effectiveness of the proposed method.

Original languageEnglish
Pages (from-to)2149-2157
Number of pages9
JournalIEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
VolumeE89-A
Issue number8
DOIs
Publication statusPublished - 2006 Aug

Keywords

  • Characteristic function
  • Contrast function
  • Independent component analysis
  • Non-Gaussianity measure

ASJC Scopus subject areas

  • Signal Processing
  • Computer Graphics and Computer-Aided Design
  • Electrical and Electronic Engineering
  • Applied Mathematics

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