Boolean matrix factorization based on collaborative neurodynamic optimization with Boltzmann machines

Xinqi LI, Jun WANG, Sam KWONG

Research output: Journal PublicationsJournal Article (refereed)peer-review

6 Citations (Scopus)


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.
Original languageEnglish
Pages (from-to)142-151
JournalNeural Networks
Early online date9 Jun 2022
Publication statusPublished - Sept 2022
Externally publishedYes

Bibliographical note

This work was supported in part by the Key Project of Science and Technology Innovation 2030 supported by the Ministry of Science and Technology of China (Grants No. 2018AAA0100300 and No. 2018AAA0101301); and by the Research Grants Council of the Hong Kong Special Administrative Region of China under GRF Grants 11208517, 11202318, 11202019, 11209819, and 11203820.


  • Boltzmann machines
  • Boolean matrix factorization
  • Collaborative neurodynamic optimization


Dive into the research topics of 'Boolean matrix factorization based on collaborative neurodynamic optimization with Boltzmann machines'. Together they form a unique fingerprint.

Cite this