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Proceedings Paper

Underdetermined blind source separation based on fuzzy C-means clustering and sparse representation
Author(s): Chaozhu Zhang; Cui Zheng
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Paper Abstract

Traditional blind source separation is based on over- determined, but the underdetermined is more consistent with actual situation, based on sparse representation, Bofill proposed "two step" method to solve the problem under some assumptions. The accuracy of the mixture affects the recovery of sources, avoiding the subjectivity of choosing parameter, using the fuzzy C-means clustering to get the mixing matrix estimation; at the same time, to lessen the requirement of sparsity, combining ICA with SCA, based on the criterion of negentropy, sources can be separated. The test shows that the algorithm proposed here get a good result.

Paper Details

Date Published: 1 October 2011
PDF: 8 pages
Proc. SPIE 8285, International Conference on Graphic and Image Processing (ICGIP 2011), 82856U (1 October 2011); doi: 10.1117/12.913501
Show Author Affiliations
Chaozhu Zhang, Harbin Engineering Univ. (China)
Cui Zheng, Harbin Engineering Univ. (China)

Published in SPIE Proceedings Vol. 8285:
International Conference on Graphic and Image Processing (ICGIP 2011)
Yi Xie; Yanjun Zheng, Editor(s)

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