Robust independent component analysis

Sajjad H. Baloch*, Hamid Krim, Marc G. Genton

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

6 Scopus citations

Abstract

Independent Component Analysis (ICA) attempts to separate independent components present in the mixture signals. Several criteria have been suggested for ICA in the past, including kurtosis and negentropy. Kurtosis suffers from a drawback of being outlier sensitive. As a remedy, we propose Robust ICA (RICA), which employs appropriate robust estimators. In this paper, we compare the robustness properties of RICA with kurtosis- and negentropy-based ICA. Since robust estimators are insensitive to outliers in contrast to maximum likelihood estimates (MLE), we demonstrate that in the presence of outliers, RICA works better than kurtosis- and negentropy-based ICA.

Original languageEnglish (US)
Title of host publication2005 IEEE/SP 13th Workshop on Statistical Signal Processing - Book of Abstracts
Pages61-64
Number of pages4
Volume2005
StatePublished - 2005
Externally publishedYes
Event2005 IEEE/SP 13th Workshop on Statistical Signal Processing - Bordeaux, France
Duration: Jul 17 2005Jul 20 2005

Other

Other2005 IEEE/SP 13th Workshop on Statistical Signal Processing
CountryFrance
CityBordeaux
Period07/17/0507/20/05

ASJC Scopus subject areas

  • Signal Processing

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