Mixing Matrix Estimation From Sparse Mixtures With Unknown Number of Sources

Zhou, Guoxu, Yang, Zuyuan, Xie, Shengli and Yang, Jun-Mei 2011, Mixing Matrix Estimation From Sparse Mixtures With Unknown Number of Sources, IEEE Transactions on Neural Networks, vol. 22, no. 2, pp. 211-221, doi: 10.1109/TNN.2010.2091427.

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Title Mixing Matrix Estimation From Sparse Mixtures With Unknown Number of Sources
Author(s) Zhou, Guoxu
Yang, Zuyuan
Xie, Shengli
Yang, Jun-Mei
Journal name IEEE Transactions on Neural Networks
Volume number 22
Issue number 2
Start page 211
End page 221
Total pages 11
Publisher IEEE
Place of publication Piscataway, N. J.
Publication date 2011
ISSN 1045-9227
Keyword(s) Blind source separation
Particle swarm optimization
Sparse component analysis
Summary In blind source separation, many methods have been proposed to estimate the mixing matrix by exploiting sparsity. However, they often need to know the source number a priori, which is very inconvenient in practice. In this paper, a new method, namely nonlinear projection and column masking (NPCM), is proposed to estimate the mixing matrix. A major advantage of NPCM is that it does not need any knowledge of the source number. In NPCM, the objective function is based on a nonlinear projection and its maxima just correspond to the columns of the mixing matrix. Thus a column can be estimated first by locating a maximum and then deflated by a masking operation. This procedure is repeated until the evaluation of the objective function decreases to zero dramatically. Thus the mixing matrix and the number of sources are estimated simultaneously. Because the masking procedure may result in some small and useless local maxima, particle swarm optimization (PSO) is introduced to optimize the objective function. Feasibility and efficiency of PSO are also discussed. Comparative experimental results show the efficiency of NPCM, especially in the cases where the number of sources is unknown and the sources are relatively less sparse.
Language eng
DOI 10.1109/TNN.2010.2091427
Field of Research 089999 Information and Computing Sciences not elsewhere classified
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category C1.1 Refereed article in a scholarly journal
Copyright notice ©2011, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30059342

Document type: Journal Article
Collection: School of Information Technology
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