DocumentCode
1188191
Title
Local minima of information-theoretic criteria in blind source separation
Author
Pham, Dinh-Tuan ; Vrins, Frédé
Author_Institution
UCL Machine Learning Group, Univ. Catholique de Louvain, Louvain-la-Neuve, Belgium
Volume
12
Issue
11
fYear
2005
Firstpage
788
Lastpage
791
Abstract
Recent simulation results have indicated that spurious minima in information-theoretic criteria with an orthogonality constraint for blind source separation may exist. Nevertheless, those results involve approximations (e.g., density estimation), so that they do not constitute an absolute proof. In this letter, the problem is tackled from a theoretical point of view. An example is provided for which it is rigorously proved that spurious minima can exist in both mutual information and negentropy optima. The proof is based on a Taylor expansion of the entropy.
Keywords
blind source separation; independent component analysis; minimisation; minimum entropy methods; BSS; Taylor expansion; blind source separation; independent component analysis; minimisation; mutual information-theoretic criteria; negentropy; Blind source separation; Cost function; Data mining; Entropy; Independent component analysis; Iterative algorithms; Mutual information; Source separation; Taylor series; Vectors; Blind source separation (BSS); entropy; independent component analysis; mutual information;
fLanguage
English
Journal_Title
Signal Processing Letters, IEEE
Publisher
ieee
ISSN
1070-9908
Type
jour
DOI
10.1109/LSP.2005.856868
Filename
1518902
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