Boolean matrix factorization based on collaborative neurodynamic optimization with Boltzmann machines

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. (c) 2022 Elsevier Ltd. All rights reserved.

Automated summary

This paper presents a collaborative neurodynamic approach to Boolean matrix factorization, which minimizes the Hamming distance between the given data matrix and its reconstruction. Experimental results demonstrate that the proposed approach outperforms six baseline methods on ten benchmark datasets in terms of convergence and performance.