A Discrete-Time Neurodynamic Approach to Sparsity-Constrained Nonnegative Matrix Factorization.

Neural Computation
Xinqi LiSam Kwong

Abstract

Sparsity is a desirable property in many nonnegative matrix factorization (NMF) applications. Although some level of sparseness of NMF solutions can be achieved by using regularization, the resulting sparsity depends highly on the regularization parameter to be valued in an ad hoc way. In this letter we formulate sparse NMF as a mixed-integer optimization problem with sparsity as binary constraints. A discrete-time projection neural network is developed for solving the formulated problem. Sufficient conditions for its stability and convergence are analytically characterized by using Lyapunov's method. Experimental results on sparse feature extraction are discussed to substantiate the superiority of this approach to extracting highly sparse features.

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Methods Mentioned

BETA
feature extraction

Software Mentioned

DTPNN
Matlab Toolbox

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