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A convex geometry-based blind source separation method for separating nonnegative sources

Yang, Zuyuan, Xiang, Yong, Rong, Yue and Xie, Kan 2015, A convex geometry-based blind source separation method for separating nonnegative sources, IEEE trans neural networks and learning systems, vol. 26, no. 8, pp. 1635-1644, doi: 10.1109/TNNLS.2014.2350026.

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Title A convex geometry-based blind source separation method for separating nonnegative sources
Author(s) Yang, Zuyuan
Xiang, YongORCID iD for Xiang, Yong orcid.org/0000-0003-3545-7863
Rong, Yue
Xie, Kan
Journal name IEEE trans neural networks and learning systems
Volume number 26
Issue number 8
Start page 1635
End page 1644
Total pages 10
Publisher IEEE
Place of publication Piscataway, N.J.
Publication date 2015-08
ISSN 2162-2388
Keyword(s) blind source separation (BSS)
convex geometry (CG)
correlated sources
nonnegative sources
Science & Technology
Technology
Computer Science, Artificial Intelligence
Computer Science, Hardware & Architecture
Computer Science, Theory & Methods
Engineering, Electrical & Electronic
Computer Science
Engineering
INDEPENDENT COMPONENT ANALYSIS
MATRIX FACTORIZATION
SPARSE REPRESENTATION
ALGORITHM
STATISTICS
CRITERION
MIXTURES
Summary This paper presents a convex geometry (CG)-based method for blind separation of nonnegative sources. First, the unaccessible source matrix is normalized to be column-sum-to-one by mapping the available observation matrix. Then, its zero-samples are found by searching the facets of the convex hull spanned by the mapped observations. Considering these zero-samples, a quadratic cost function with respect to each row of the unmixing matrix, together with a linear constraint in relation to the involved variables, is proposed. Upon which, an algorithm is presented to estimate the unmixing matrix by solving a classical convex optimization problem. Unlike the traditional blind source separation (BSS) methods, the CG-based method does not require the independence assumption, nor the uncorrelation assumption. Compared with the BSS methods that are specifically designed to distinguish between nonnegative sources, the proposed method requires a weaker sparsity condition. Provided simulation results illustrate the performance of our method.
Language eng
DOI 10.1109/TNNLS.2014.2350026
Field of Research 080199 Artificial Intelligence and Image Processing not elsewhere classified
Socio Economic Objective 861799 Communication Equipment not elsewhere classified
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2015, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30075035

Document type: Journal Article
Collection: School of Information Technology
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