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An Introduction to Independent Component Analysis: InfoMax and FastICA algorithms

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Doi: 10.20982/tqmp.06.1.p031

Langlois, Dominic , Chartier, Sylvain , Gosselin, Dominique
31-38
Keywords: Statistics , Independent component analysis
Tools: Mathematica
(no sample data)   (Appendix)

This paper presents an introduction to independent component analysis (ICA). Unlike principal component analysis, which is based on the assumptions of uncorrelatedness and normality, ICA is rooted in the assumption of statistical independence. Foundations and basic knowledge necessary to understand the technique are provided hereafter. Also included is a short tutorial illustrating the implementation of two ICA algorithms (FastICA and InfoMax) with the use of the Mathematica software.


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