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dc.contributor.authorYang, Z.
dc.contributor.authorXiang, Y.
dc.contributor.authorRong, Yue
dc.contributor.authorXie, K.
dc.identifier.citationYang, Z. and Xiang, Y. and Rong, Y. and Xie, K. 2014. A convex geometry-based blind source separation method for separating nonnegative sources. IEEE Transactions on Neural Networks and Learning Systems. 26 (8): pp.1635-1644.

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 hullspanned by the mapped observations. Considering these zerosamples, 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.

dc.publisherInstitute of Electrical and Electronics Engineers
dc.subjectcorrelated sources
dc.subjectconvex geometry (CG)
dc.subjectBlind source separation (BSS)
dc.subjectnonnegative sources
dc.titleA convex geometry based blind source separation method for separating nonnegative sources
dc.typeJournal Article
dcterms.source.titleIEEE Transactions on Neural Networks and Learning Systems

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curtin.departmentDepartment of Electrical and Computer Engineering
curtin.accessStatusOpen access

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