Non-negative matrix factorization with L 0 sparseness constraints and its applications to face recognition

Abstract

In order to solve the problem of unstable sparseness of non-negative matrix factorization (NMF), the improved NMF algorithms with L₀ sparseness constraints are proposed. With the constraining the L₀ norm of the coefficient matrix, we applied inverse matching principle into non-negative least square (ISNNLS) which enhances the reconstruction ability of the decomposition matrix. In addition, the L₀ sparseness constraints are added to the basis matrix. In the updating process, the proposed algorithm set the smallest value to zero by projecting the basis vectors onto the closest non-negative vector with the expected sparseness. The experimental results have illustrated that the proposed algorithm can achieve higher reconstruction quality and effectiveness compared with the other algorithms.

Keywords

Sparseness NMF sparse coding feature extraction

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