Non-Negative Matrix Factorization and Its Application in Blind Sparse Source Separation with Less Sensors Than Sources
نویسندگان
چکیده
Non-Negative Matrix Factorization (NMF) implies that a given nonnegative matrix is represented by a product of two non-negative matrices. In this paper, a factorization condition (consistent condition) on basis matrix is proposed firstly. For a given consistent basis matrix, although there exist infinite solutions (factorizations) generally, the sparse solution is unique with probability one, which can be obtained by solving linear programming problems. Although it is very difficult to find out the best basis matrix, an algorithm is developed for finding a suboptimal basis matrix. Finally, an application of NMF is proposed for blind sparse source separation with less sensors than sources.
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