new e-publication: ICA survey

[Arun Jagota]

New refereed e-publication 
action editor: Barak Pearlmutter

A. Hyvarinen, Survey on Independent Component Analysis,
Neural Computing Surveys 2, 94--128, 1999. 150 references.

http://www.icsi.berkeley.edu/~jagota/NCS

Abstract:

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.