DocumentCode
2946811
Title
SWM : a class of convex contrasts for source separation
Author
Vrins, Frédéric ; Verleysen, Michel ; Jutten, Christian
Author_Institution
UCL Machine Learning Group, Univ. catholique de Louvain, Louvain-la-Neuve, Belgium
Volume
5
fYear
2005
fDate
18-23 March 2005
Abstract
We derive a class of contrasts for blind source separation (BSS) to separate bounded sources (or more generally, finite sources), based on support width measures (SWM) of the marginal output distributions. These contrasts are shown to have no spurious local maxima, i.e., all the local maxima are relevant from the source separation point of view; they all correspond to non-mixing BSS solutions so that a gradient-ascent method can be used.
Keywords
blind source separation; gradient methods; BSS; blind source separation; convex contrasts; gradient-ascent method; support width measures; Blind source separation; Gaussian distribution; Independent component analysis; Machine learning; Scattering; Source separation;
fLanguage
English
Publisher
ieee
Conference_Titel
Acoustics, Speech, and Signal Processing, 2005. Proceedings. (ICASSP '05). IEEE International Conference on
ISSN
1520-6149
Print_ISBN
0-7803-8874-7
Type
conf
DOI
10.1109/ICASSP.2005.1416265
Filename
1416265
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