Projection-pursuit-based method for blind separation of nonnegative sources

Yang, Zuyuan, Xiang, Yong, Rong, Yue and Xie, Shengli 2013, Projection-pursuit-based method for blind separation of nonnegative sources, IEEE transactions on neural networks and learning systems, vol. 24, no. 1, pp. 47-57, doi: 10.1109/TNNLS.2012.2224124.

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Title Projection-pursuit-based method for blind separation of nonnegative sources
Author(s) Yang, Zuyuan
Xiang, YongORCID iD for Xiang, Yong
Rong, Yue
Xie, Shengli
Journal name IEEE transactions on neural networks and learning systems
Volume number 24
Issue number 1
Start page 47
End page 57
Total pages 11
Publisher IEEE
Place of publication Piscataway, N.J.
Publication date 2013
ISSN 2162-237X
Keyword(s) blind source separation
linear programming (LP)
nonnegative sources
projection pursuit (PP)
Summary This paper presents a projection pursuit (PP) based method for blind separation of nonnegative sources. First, the available observation matrix is mapped to construct a new mixing model, in which the inaccessible source matrix is normalized to be column-sum-to-1. Then, the PP method is proposed to solve this new model, where the mixing matrix is estimated column by column through tracing the projections to the mapped observations in specified directions, which leads to the recovery of the sources. The proposed method is much faster than Chan's method, which has similar assumptions to ours, due to the usage of optimal projection. It is also more advantageous in separating cross-correlated sources than the independence- and uncorrelation-based methods, as it does not employ any statistical information of the sources. Furthermore, the new method does not require the mixing matrix to be nonnegative. Simulation results demonstrate the superior performance of our method.
Language eng
DOI 10.1109/TNNLS.2012.2224124
Field of Research 090609 Signal Processing
Socio Economic Objective 890104 Mobile Telephone Networks and Services
HERDC Research category C1 Refereed article in a scholarly journal
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Document type: Journal Article
Collection: School of Information Technology
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