Sparsity induced convex nonnegative matrix factorization algorithm with manifold regularization

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 L 2,1 nor...

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Bibliographic Details
Published inTongxin Xuebao Vol. 41; pp. 84 - 95
Main Authors Feiyue QIU, Bowen CHEN, Tieming CHEN, Guodao ZHANG
Format Journal Article
LanguageChinese
Published Editorial Department of Journal on Communications 01.05.2020
Subjects
nonnegative matrix factorization;manifold regularization;sparse constraint;K-means clustering
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Summary: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 L 2,1 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.
ISSN:1000-436X