Independent Component Analysis (ICA) and Method of Estimating Functions

Shun-ichi AMARI  

IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences   Vol.E85-A   No.3   pp.540-547
Publication Date: 2002/03/01
Online ISSN: 
Print ISSN: 0916-8508
Type of Manuscript: INVITED PAPER (Special Section on the Trend of Digital Signal Processing and Its Future Direction)
Category: Theories
independent components,  information geometry,  estimating functions,  learning,  signal processing,  

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Independent component analysis (ICA) is a new method of extracting independent components from multivariate data. It can be applied to various fields such as vision and auditory signal analysis, communication systems, and biomedical and brain engineering. There have been proposed a number of algorithms. The present article shows that most of them use estimating functions from the statistical point of view, and give a unified theory, based on information geometry, to elucidate the efficiency and stability of the algorithms. This gives new efficient adaptive algorithms useful for various problems.