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
Link To Document :
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