A nonnegative matrix factorization algorithm based on a discrete-time projection neural network

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

26 Scopus Citations
View graph of relations

Detail(s)

Original languageEnglish
Pages (from-to)63-71
Journal / PublicationNeural Networks
Volume103
Online published20 Mar 2018
Publication statusPublished - Jul 2018

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.

Research Area(s)

  • Biconvex optimization, Discrete-time projection neural network, Nonnegative matrix factorization