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28th Annual Conference on Current
Trends in
Theory and Practice of Informatics
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November 24 - December 1, 2001
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Fast Independent Component Analysis in Kernel Feature Spaces
by Andras Kocsor and Janos Csirik
Abstract:
It is common practice to apply linear or nonlinear feature
extraction methods before classification. Usually linear methods
are faster and simpler than nonlinear ones but an idea
successfully employed in the nonlinearization of Support Vector
Machines permits a simple and effective extension of several
statistical methods to their nonlinear counterparts. In this paper
we follow this general nonlinearization approach in context of
Independent Component Analysis, which is a general purpose
statistical method for blind source separation and feature
extraction. In addition nonlinearized formulae are furnished along
with illustration the usefulness of the proposed method as an
unsupervised feature extractor for the classification of Hungarian
phonemes.