Sparsity induced convex nonnegative matrix factorization algorithm with manifold regularization

被引：0

作者：

Qiu F. ^{[1
,2
]}

Chen B. ^{[2
]}

Chen T. ^{[2
]}

Zhang G. ^{[2
]}

机构：

[1] College of Education Science and Technology, Zhejiang University of Technology, Hangzhou

[2] College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou

来源：

Tongxin Xuebao/Journal on Communications | 2020年 / 41卷 / 05期

基金：

中国国家自然科学基金;

关键词：

K-means clustering; Manifold regularization; Nonnegative matrix factorization; Sparse constraint;

D O I：

10.11959/j.issn.1000-436x.2020064

中图分类号：

学科分类号：

摘要：

To address problems that the effectiveness of feature learned from real noisy data by classical nonnegative matrix factorization method, a novel sparsity induced manifold regularized convex nonnegative matrix factorization algorithm (SGCNMF) was proposed. Based on manifold regularization, the L2, 1 norm was introduced to the basis matrix of low dimensional subspace as sparse constraint. The multiplicative update rules were given and the convergence of the algorithm was analyzed. Clustering experiment was designed to verify the effectiveness of learned features within various of noisy environments. The empirical study based on K-means clustering shows that the sparse constraint reduces the representation of noisy features and the new method is better than the 8 similar algorithms with stronger robustness to a variable extent. © 2020, Editorial Board of Journal on Communications. All right reserved.

引用

页码：84 / 95

页数：11

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