Performance comparison of new nonparametric independent component analysis algorithm for different entropic indexes


SEVİM Y. , ATASOY A.

TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES, vol.20, no.3, pp.287-297, 2012 (Journal Indexed in SCI) identifier identifier

  • Publication Type: Article / Article
  • Volume: 20 Issue: 3
  • Publication Date: 2012
  • Doi Number: 10.3906/elk-1004-1
  • Title of Journal : TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES
  • Page Numbers: pp.287-297

Abstract

Most independent component analysis (ICA) algorithms use mutual information (MI) measures based on Shannon entropy as a cost function, but Shannon entropy is not the only measure in the literature. In this paper, instead of Shannon entropy, Tsallis entropy is used and a novel ICA algorithm, which uses kernel density estimation (KDE) for estimation of source distributions, is proposed. KDE is directly evaluated from the original data samples, so it solves the important problem in ICA: how to choose nonlinear functions as the probability density function (pdf) estimation of the sources.