An extension to the natural gradient algorithm for robust independent component analysis in the presence of outliers

Muhammad Tufail, Masahide Abe, Masayuki Kawamata

Research output: Contribution to journalArticlepeer-review

Abstract

In this paper, we propose to employ an extension to the natural gradient algorithm for robust Independent Component Analysis against outliers. The standard natural gradient algorithm does not exhibit this property since it employs nonrobust sample estimates for computing higher order moments. In order to overcome this drawback, we propose to use robust alternatives to higher order moments, which are comparatively less sensitive to outliers in the observed data. Some computer simulations are presented to show that the proposed method, as compared to the standard natural gradient algorithm, gives better performance in the presence of outlying data.

Original languageEnglish
Pages (from-to)2429-2432
Number of pages4
JournalIEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
VolumeE89-A
Issue number9
DOIs
Publication statusPublished - 2006 Sep

Keywords

  • Independent Component Analysis
  • Outliers
  • Robust higher order statistics

ASJC Scopus subject areas

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

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