A Parallel Dual Matrix Method for Blind Signal Separation

A parallel dual matrix method that considers all cases of numerical relations between a mixing matrix and a separating matrix is proposed in this letter. Different constrained terms are used to construct cost function for every subalgorithm. These constrained terms reflect numerical relation. Theref...

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Bibliographic Details
Published in:Neural computation 2014-03, Vol.26 (3), p.592-610
Main Authors: Zeng, T. J., Feng, Q. Y.
Format: Article
Language:English
Subjects:
Algorithms
Computer simulation
Letters
Matrix
Neurosciences
Numerical analysis
Signaling
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Summary:A parallel dual matrix method that considers all cases of numerical relations between a mixing matrix and a separating matrix is proposed in this letter. Different constrained terms are used to construct cost function for every subalgorithm. These constrained terms reflect numerical relation. Therefore, a number of undesired solutions are excluded, the search region is reduced, and the convergence efficiency of the algorithm is ultimately improved. Moreover, any parallel subalgorithm is proven to converge to a desired separating matrix only if its cost function converges to zero. Computer simulations indicate that the algorithm efficiently performs blind signal separation.
ISSN:0899-7667
1530-888X
DOI:10.1162/NECO_a_00554