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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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| Published in: | Neural computation 2014-03, Vol.26 (3), p.592-610 |
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| Main Authors: | , |
| Format: | Article |
| Language: | English |
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| cited_by | cdi_FETCH-LOGICAL-c409t-3566f5cdd6aa9f0d3ed3b7f3b48d98806a3516d7a34655b4248ccb20029d8c933 |
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| cites | cdi_FETCH-LOGICAL-c409t-3566f5cdd6aa9f0d3ed3b7f3b48d98806a3516d7a34655b4248ccb20029d8c933 |
| container_end_page | 610 |
| container_issue | 3 |
| container_start_page | 592 |
| container_title | Neural computation |
| container_volume | 26 |
| creator | Zeng, T. J. Feng, Q. Y. |
| description | 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. |
| doi_str_mv | 10.1162/NECO_a_00554 |
| format | article |
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| ispartof | Neural computation, 2014-03, Vol.26 (3), p.592-610 |
| issn | 0899-7667 1530-888X |
| language | eng |
| recordid | cdi_pubmed_primary_24320846 |
| source | MIT Press Journals |
| subjects | Algorithms Computer simulation Letters Matrix Neurosciences Numerical analysis Signaling |
| title | A Parallel Dual Matrix Method for Blind Signal Separation |
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