.png){kind=icon}
返回
Binary matrix factorization via collaborative neurodynamic optimization
.png){kind=icon}
PRE
AI
DOI：10.1016/j.neunet.2024.106348.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Binary matrix factorization is an important tool for dimension reduction for high -dimensional datasets with binary attributes and has been successfully applied in numerous areas. This paper presents a collaborative neurodynamic optimization approach to binary matrix factorization based on the original combinatorial optimization problem formulation and quadratic unconstrained binary optimization problem reformulations. The proposed approach employs multiple discrete Hopfield networks operating concurrently in search of local optima. In addition, a particle swarm optimization rule is used to reinitialize neuronal states iteratively to escape from local minima toward better ones. Experimental results on eight benchmark datasets are elaborated to demonstrate the superior performance of the proposed approach against six baseline algorithms in terms of factorization error. Additionally, the viability of the proposed approach is demonstrated for pattern discovery on three datasets.
Keyword:
Binary matrix factorization
Collaborative neurodynamic optimization
Discrete Hopfield network
Quadratic unconstrained binary optimization
Pattern discovery
期刊
IF:
6.3
论文数:
8.0K
被引数:
3.0W
机构
引用论文
A nonnegative matrix factorization algorithm based on a discrete-time projection neural network.png)
NEURAL NETWORKS
NEURAL NETWORKSIF6.3
PREAI
分享收藏Effect of volume fiber and crack length on interlaminar fracture properties of glass fiber reinforced polyester composites (GF/PO composites)Mechanics
OAAI
MechanicsIF0
OAAI分享收藏An event-triggered collaborative neurodynamic approach to distributed global optimization分布式全局优化的事件触发协作神经动力学方法NEURAL NETWORKSOAAI
NEURAL NETWORKSIF6.3
OAAI分享收藏Two-timescale recurrent neural networks for distributed minimax optimizationNEURAL NETWORKS
NEURAL NETWORKSIF6.3
PREAI
分享收藏Cardinality-constrained portfolio selection via two-timescale duplex neurodynamic optimizationNEURAL NETWORKSOAAI
NEURAL NETWORKSIF6.3
OAAI分享收藏
查看更多内容