Department of Computer Science, University of Exeter, United Kingdom.
Department of Computer Science, City University of Hong Kong, Kowloon, Hong Kong; School of Data Science, City University of Hong Kong, Kowloon, Hong Kong.
Neural Netw. 2022 Sep;153:142-151. doi: 10.1016/j.neunet.2022.06.006. Epub 2022 Jun 9.
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.
本文提出了一种协同神经动力学方法来进行布尔矩阵分解。基于一种二进制优化公式来最小化给定数据矩阵与其低秩重构之间的汉明距离，所提出的方法采用了一群同时运行的玻尔兹曼机来进行分解解决方案的分散搜索。此外，还使用了粒子群优化规则来重新初始化玻尔兹曼机的神经元状态，以避免局部收敛时陷入局部最小值，从而找到全局解。实验结果表明，在所提出的方法与六个基准方法在十个基准数据集上的对比中，该方法具有优越的收敛性和性能。
