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

Authors

YUSUF SEVİM
AYTEN ATASOY

DOI

10.3906/elk-1004-1

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.

Keywords

Independent component analysis (ICA), kernel density estimation (KDE), shannon entropy, tsallis entropy, mutualinformation (MI), nonparametric ICA (NpICA)

First Page

287

Last Page

297

Recommended Citation

SEVİM, YUSUF and ATASOY, AYTEN (2012) "Performance comparison of new nonparametric independent component analysis algorithm for different entropic indexes," Turkish Journal of Electrical Engineering and Computer Sciences: Vol. 20: No. 3, Article 2. https://doi.org/10.3906/elk-1004-1
Available at: https://journals.tubitak.gov.tr/elektrik/vol20/iss3/2