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

journal contribution
posted on 2015-08-01, 00:00 authored by Zuyuan Yang, Yong XiangYong Xiang, Y Rong, K Xie
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.

History

Journal

IEEE trans neural networks and learning systems

Volume

26

Issue

8

Pagination

1635 - 1644

Publisher

IEEE

Location

Piscataway, N.J.

eISSN

2162-2388

Language

eng

Publication classification

C Journal article; C1 Refereed article in a scholarly journal

Copyright notice

2015, IEEE