NDLI: A Contrast Function for Independent Component Analysis Without Permutation Ambiguity

Content Provider	IEEE Xplore Digital Library
Author	Zarzoso, V. Comon, P. Phlypo, R.
Copyright Year	1990
Abstract	This brief deals with the problem of blind source separation (BSS) via independent component analysis (ICA). We prove that a linear combination of the separator output fourth-order marginal cumulants (kurtoses) is a valid contrast function for ICA under prewhitening if the weights have the same sign as the source kurtoses. If, in addition, the source kurtoses are different and so are the linear combination weights, the contrast eliminates the permutation ambiguity typical to ICA, as the estimated sources are sorted at the separator output according to their kurtosis values in the same order as the weights. If the weights equal the source kurtoses, the contrast is a cumulant matching criterion based on the maximum-likelihood principle. The contrast can be maximized by means of a cost-efficient Jacobi-type pairwise iteration. In the real-valued two-signal case, the asymptotic variance of the resulting Givens angle estimator is determined in closed form, leading to the contrast weights with optimal finite-sample performance. A fully blind solution can be implemented by computing the optimum weights from the initial source estimates obtained by a classical ICA stage. An experimental study validates the features of the proposed technique and shows its superior performance compared to related previous methods.
Sponsorship	IEEE Computational Intelligence Society
Page Count	6
File Size	263573
Starting Page	863
Ending Page	868
File Format	PDF
ISSN	10459227
Volume Number	21
Issue Number	5
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-05-01
Publisher Place	U.S.A.
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Independent component analysis Source separation Particle separators Jacobian matrices Blind source separation Maximum likelihood estimation Performance analysis Laboratories Information systems Mutual information performance analysis Blind source separation (BSS) contrast functions independent component analysis (ICA) Jacobi optimization kurtosis
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Computer Networks and Communications Computer Science Applications Software

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