Bregman iteration algorithm for sparse nonnegative matrix factorizations via alternating l1-norm minimization

Authors:
Lingling Jiang;Haiqing Yin
Affiliations:
College of Mathematics and Computational Science, China University of Petroleum, Dongying, China 257061;Department of Applied Mathematics, Xidian University, Xi'an, China 710071
Venue:
Multidimensional Systems and Signal Processing
Year:
2012

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Abstract

Sparse nonnegative matrix factorizations can be considered as dimension reduction methods that can control the degree of sparseness of basis matrix or coefficient matrix under non-negativity constraints. In this paper, by exploring the sparsity of the basis matrix and the coefficient matrix under certain domains, we propose an alternative iteration approach with l 1-norm minimization for face recognition. Moreover, a modified version of linearized Bregman iteration is developed to efficiently solve the proposed minimization problem. Experimental results show that new algorithm is promising in terms of detection accuracy, computational efficiency.