A nonnegative matrix factorization algorithm based on a discrete-time projection neural network. (July 2018)

• Record Type: Journal Article
• Authors: Che, Hangjun; Wang, Jun
• Is Part Of: Neural networks. Volume 103(2018)
• Page Start: 63
• Page End: 71
• Publication Date: 2018-07
• Subjects: Nonnegative matrix factorization -- Discrete-time projection neural network -- Biconvex optimization; Neural computers -- Periodicals; Neural networks (Computer science) -- Periodicals; Neural networks (Neurobiology) -- Periodicals; Nervous System -- Periodicals; Ordinateurs neuronaux -- Périodiques; Réseaux neuronaux (Informatique) -- Périodiques; Réseaux neuronaux (Neurobiologie) -- Périodiques; Neural computers; Neural networks (Computer science); Neural networks (Neurobiology); Periodicals; 006.32
• DOI: 10.1016/j.neunet.2018.03.003
• Languages: English
• ISSNs: 0893-6080

Abstract

Abstract: This paper presents an algorithm for nonnegative matrix factorization based on a biconvex optimization formulation. First, a discrete-time projection neural network is introduced. An upper bound of its step size is derived to guarantee the stability of the neural network. Then, an algorithm is proposed based on the discrete-time projection neural network and a backtracking step-size adaptation. The proposed algorithm is proven to be able to reduce the objective function value iteratively until attaining a partial optimum of the formulated biconvex optimization problem. Experimental results based on various data sets are presented to substantiate the efficacy of the algorithm.