A convex geometry-based blind source separation method for separating nonnegative sources
Data(s) |
01/08/2015
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Resumo |
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. |
Identificador | |
Idioma(s) |
eng |
Publicador |
IEEE |
Relação |
http://dro.deakin.edu.au/eserv/DU:30075035/yang--convexgeometrybased-2015.pdf http://www.dx.doi.org/10.1109/TNNLS.2014.2350026 |
Direitos |
2015, IEEE |
Palavras-Chave | #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 |
Tipo |
Journal Article |