IEEE Trans Neural Netw Learn Syst - A Convex Geometry-Based Blind Source Separation Method for Separating Nonnegative Sources.

Tópicos

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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.

Resumo Limpo

paper present convex geometri cgbase method blind separ nonneg sourc first unaccess sourc matrix normal columnsumtoon map avail observ matrix zerosampl found search facet convex hull span map observ consid zerosampl quadrat cost function respect row unmix matrix togeth linear constraint relat involv variabl propos upon algorithm present estim unmix matrix solv classic convex optim problem unlik tradit blind sourc separ bss method cgbase method requir independ assumpt uncorrel assumpt compar bss method specif design distinguish nonneg sourc propos method requir weaker sparsiti condit provid simul result illustr perform method

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