关键词: 欠定盲分离, 非负矩阵分解, 稀疏性, 行列式准则

Abstract: The decomposed left matrix of Non-negative Matrix Factorization (NMF) is required to be full column rank, which limits of its application to Underdetermined Blind Source Separation (UBSS). To address this issue, an algorithm for UBSS based on determinant and sparsity constraint of NMF, named DSNMF, was proposed in this paper. On the basis of standard NMF, determinant criterion was used for constraining the left matrix of NMF, while sparsity was used for constraining the right one. In this way, the reconstruction error, the uniqueness of mixing matrix and the spasity of original sources can be equipoised, which leads to the underdetermined blind separation of mixing matrix and original sources. The simulation results show that DSNMF both works well for good and poor sparsity of sources separation.

Key words: Underdetermined Blind Source Seperation (UBSS), Non-negative Matrix Factorization (NMF), sparsity, determinant criterion