Monotonic Convergence of a Nonnegative ICA Algorithm on Stiefel Manifold

When the independent sources are known to be nonnegative and well-grounded, which means that they have a non-zero pdf in the region of zero, a few nonnegative independent component analysis (ICA) algorithms have been proposed to separate these positive sources. In this paper, by using the property o...

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Bibliographic Details
Published inNeural Information Processing pp. 1098 - 1106
Main Authors Ye, Mao, Fan, Xuqian, Liu, Qihe
Format Book Chapter
LanguageEnglish
Published Berlin, Heidelberg Springer Berlin Heidelberg 2006
SeriesLecture Notes in Computer Science
Subjects
Blind Signal
Convergence Condition
Independent Component Analysis
Learning Rate
Online AccessGet full text
ISBN3540464794
9783540464792
ISSN0302-9743
1611-3349
DOI10.1007/11893028_122

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Summary:When the independent sources are known to be nonnegative and well-grounded, which means that they have a non-zero pdf in the region of zero, a few nonnegative independent component analysis (ICA) algorithms have been proposed to separate these positive sources. In this paper, by using the property of skew-symmetry matrix, rigorous convergence proof of a nonnegative ICA algorithm on Stiefel manifold is given. And sufficient convergence conditions are presented. Simulations are employed to confirm our convergence theory. Our techniques may be useful to analyze general ICA algorithms on Stiefel manifold.
ISBN:3540464794
9783540464792
ISSN:0302-9743
1611-3349
DOI:10.1007/11893028_122