L3/2 Sparsity Constrained Graph Non-negative Matrix Factorization

摘要：

　　For enhancing the cluster accuracy,this paper presents a novel algorithm called L3/2 Sparsity Constrained Graph Non-negative Matrix Factorization(FGNMF),which based on the convex and smooth L3/2 norm.When original data is factorized in lower dimensional space using NMF,FGNMF preserves the local structure and intrinsic geometry of data,using the convex and smooth L3/2 norm as sparse constrains for the low dimensional feature.An efficient multiplicative updating procedure was produced,the relation with gradient descent method showed that the updating rules are special case of its.Compared with NMF and its improved algorithms based on sparse representation,experiment results on USPS handwrite database and COIL20 image database have shown that the proposed method achieves better clustering results.

关键词： Image Representation Non-negative Matrix Factorization (NMF) Sparse constrained Clustering

作者: Shiqiang Du Yuqing Shi Weilan Wang

作者单位: School of Mathematics and Computer Science,Northwest University for Nationalities,Lanzhou 730030,Chi School of Electrical Engineering,Northwest University for Nationalities,Lanzhou 730030,China

会议类型: 国际会议

会议名称: 第26届中国控制与决策会议(2014 CCDC)

会议地点: 长沙

会议语种:英文

页码: 2962-2965

在线出版日期: 2014-05-31（万方平台首次上网日期，不代表论文的发表时间）