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Survey on Independent Component Analysis

Aapo Hyvärinen
Helsinki University of Technology
Laboratory of Computer and Information Science
P.O.Box 5400, FIN-02015 HUT, Finland aapo/


A common problem encountered in such disciplines as statistics, data analysis, signal processing, and neural network research, is finding 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.
Neural Computing Surveys, Vol. 2, pp. 94-128, 1999.

Keywords: Independent component analysis, blind source separation, factor analysis, data analysis, higher-order statistics, neural networks, unsupervised learning, Hebbian learning

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See also the What is ICA page.


Aapo Hyvarinen