Boolean matrix factorization based on collaborative neurodynamic optimization with Boltzmann machines. (September 2022)

• Record Type: Journal Article
• Authors: Li, Xinqi; Wang, Jun; Kwong, Sam
• Is Part Of: Neural networks. Volume 153(2022)
• Page Start: 142
• Page End: 151
• Publication Date: 2022-09
• Subjects: Boolean matrix factorization -- Collaborative neurodynamic optimization -- Boltzmann machines; 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.2022.06.006
• Languages: English
• ISSNs: 0893-6080

Abstract

Abstract: This paper presents a collaborative neurodynamic approach to Boolean matrix factorization. Based on a binary optimization formulation to minimize the Hamming distance between a given data matrix and its low-rank reconstruction, the proposed approach employs a population of Boltzmann machines operating concurrently for scatter search of factorization solutions. In addition, a particle swarm optimization rule is used to re-initialize the neuronal states of Boltzmann machines upon their local convergence to escape from local minima toward global solutions. Experimental results demonstrate the superior convergence and performance of the proposed approach against six baseline methods on ten benchmark datasets.