Independent Component Analysis

Independent Component Analysis (2001)

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by Aapo Hyvärinen

Venue:	Neural Computing Surveys
Citations:	1491 - 93 self

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@ARTICLE{Hyvärinen01independentcomponent,
    author = {Aapo Hyvärinen},
    title = {Independent Component Analysis},
    journal = {Neural Computing Surveys},
    year = {2001},
    volume = {2}
}

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Abstract

A common problem encountered in such disciplines as statistics, data analysis, signal processing, and neural network research, is nding a suitable representation of multivariate data. For computational and conceptual simplicity, such a representation is often sought as a linear transformation of the original data. Well-known linear transformation methods include, for example, principal component analysis, factor analysis, and projection pursuit. A recently developed linear transformation method is independent component analysis (ICA), in which the desired representation is the one that minimizes the statistical dependence of the components of the representation. Such a representation seems to capture the essential structure of the data in many applications. In this paper, we survey the existing theory and methods for ICA. 1

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