Manifold ranking graph regularization non-negative matrix factorization with global and local structures

被引：0
作者：
Xiangli Li
Jianglan Yu
Xiaoliang Dong
Pengfei Zhao
机构：
[1] Guilin University of Electronic Technology,School of Mathematics and Computing Science
[2] Guilin University of Electronic Technology,Guangxi Key Laboratory of Cryptography and Information Security
[3] Guilin University of Electronic Technology,Guangxi Key Laboratory of Automatic Detecting Technology and Instruments
[4] Guilin University of Electronic Technology,Guangxi Colleges and Universities Key Laboratory of Data Analysis and Computation
[5] Beifang University of Nationalities,School of Mathematics and Information
[6] Southwest Jiaotong University,School of Mathematics
来源：
Pattern Analysis and Applications | 2020年 / 23卷
关键词：
Non-negative matrix factorization; Clustering; Manifold ranking; Regularization;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
Non-negative matrix factorization (NMF) is a recently popularized technique for learning parts-based, linear representations of non-negative data. Although the decomposition rate of NMF is very fast, it still suffers from the following deficiency: It only revealed the local geometry structure; global geometric information of data set is ignored. This paper proposes a manifold ranking graph regularization non-negative matrix factorization with local and global geometric structure (MRLGNMF) to overcome the above deficiency. In particular, MRLGNMF induces manifold ranking to the non-negative matrix factorization with Sinkhorn distance. Numerical results show that the new algorithm is superior to the existing algorithm.
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页码：967 / 974
页数：7
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