ICA paper available on-line.

[Te-Won Lee]

		Paper available on-line


	"A Unifying Information-theoretic Framework 
	   for Independent Component Analysis"

	T-W. Lee, M. Girolami, A.J. Bell and T.J. Sejnowski.
	International Journal on Mathematical and Computer Modeling
	(in press)

	http://www.cnl.salk.edu/~tewon/Public/mcm.ps.gz
	(130k, 23 pages)

Abstract:

We show that different theories recently proposed for Independent
Component Analysis (ICA) lead to the same iterative learning algorithm
for blind separation of mixed independent sources.  We review those
theories and suggest that information theory can be used to unify
several lines of research. Pearlmutter and Parra (1996) and Cardoso
(1997) showed that the infomax approach of Bell and Sejnowski (1995)
and the maximum likelihood estimation approach are equivalent. We show
that negentropy maximization also has equivalent properties and
therefore all three approaches yield the same learning rule for a
fixed nonlinearity.  Girolami and Fyfe (1997a) have shown that the
nonlinear Principal Component Analysis (PCA) algorithm of Karhunen and
Joutsensalo (1994) and Oja (1997) can also be viewed from
information-theoretic principles since it minimizes the sum of squares
of the fourth-order marginal cumulants and therefore approximately
minimizes the mutual information (Comon, 1994). Lambert (1996) has
proposed different Bussgang cost functions for multichannel blind
deconvolution. We show how the Bussgang property relates to the
infomax principle.  Finally, we discuss convergence and stability as
well as future research issues in blind source separation.

 


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Dr. Te-Won Lee                   EMAIL: tewon at salk.edu
Computational Neurobiology Lab,   WORK: (619) 453-4100 x1215 
Salk Institute,                   HOME: (619) 450-9036 
10010 N. Torrey Pines Rd.          FAX: (619) 587-0417
La Jolla, CA  92037                WEB: http://www.cnl.salk.edu/~tewon 
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