Sparse Nonnegative Matrix Factorization Based on Collaborative Neurodynamic Optimization

被引：0
作者：
Che, Hangjun [1 ,2 ]
Wang, Jun [1 ,2 ,3 ]
机构：
[1] City Univ Hong Kong, Dept Comp Sci, Hong Kong, Peoples R China
[2] City Univ Hong Kong, Shenzhen Res Inst, Shenzhen, Hong Kong, Peoples R China
[3] City Univ Hong Kong, Sch Data Sci, Hong Kong, Peoples R China
来源：
2019 9TH INTERNATIONAL CONFERENCE ON INFORMATION SCIENCE AND TECHNOLOGY (ICIST2019) | 2019年
基金：
中国国家自然科学基金;
关键词：
Sparse nonnegative matrix factorization; Bilevel optimization; Collaborative neurodynamic approach; RECURRENT NEURAL-NETWORK; CONVEX-OPTIMIZATION; MULTIAGENT SYSTEM; ALGORITHMS; SUBJECT;
D O I：
10.1109/icist.2019.8836758
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
This paper presents a collaborative neurodynamic approach to sparse nonnegative matrix factorization (SNMF). SNMF is formulated as a bilevel optimization problem. In the lower level of the problem, the sparsity of factorized matrix is minimized subject to the factorization error and nonnegative constraints. In the upper level of the problem, the parameter of the inverted Gaussian function is minimized to approximate l(0) norm. A group of neurodynamic models operating at two timescales is employed to solve the reformulated problem. The experimental results show the superiority of the proposed approach.
引用
收藏
页码：114 / 121
页数：8
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