NON-NEGATIVE MATRIX FACTORIZATION BASED ON LOCALLY LINEAR EMBEDDING

摘要：

　　In this paper,we proposed a novel method called Nonnegative Matrix Factorization based on Locally Linear Embedding (LLE-NMF).This idea is to factorize the nonnegative matrix considering the intrinsic geometric structure of the high dimensional data.Instead of the need to estimate pairwise distances between widely separated data points,LLE-NMF is able to find a compact representation recovering the global nonlinear structure from locally linear fits.We proposed updating rules and simulation results.In the experiments,we show the encouraging results of the method in comparison to the state-of-the-art algorithms on face image clustering.

关键词： Non-negative Matrix Factorization Locally Linear Embedding Clustering

作者: Congying Han Guangqi Shao Yong A Yong A Tiande Guo

作者单位: School of Mathematical Science, UCAS, Beijing 100049, China

会议类型: 国际会议

会议名称: 11th International Symposium on Operations Research and its Applications(第11届运筹学及其应用国际研讨会)

会议地点: 安徽黄山

会议语种:英文

页码: 132-135

在线出版日期: 2013-08-23（万方平台首次上网日期，不代表论文的发表时间）