A convex geometry based blind source separation method for separating nonnegative sources
|dc.identifier.citation||Yang, 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.publisher||Institute of Electrical and Electronics Engineers|
|dc.subject||convex geometry (CG)|
|dc.subject||Blind source separation (BSS)|
|dc.title||A convex geometry based blind source separation method for separating nonnegative sources|
|dcterms.source.title||IEEE Transactions on Neural Networks and Learning Systems|
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|curtin.department||Department of Electrical and Computer Engineering|