MULTI-VIEW SUBSPACE CLUSTERING WITH LOCAL AND GLOBAL INFORMATION

被引:3
作者
Duan, Yi-Qiang [1 ]
Yuan, Hao-Liang [1 ]
Lai, Loi Lei [1 ]
He, Ben [2 ]
机构
[1] Guangdong Univ Technol, Sch Automat, Guangzhou, Peoples R China
[2] Univ Chinese Acad Sci, Sch Comp Sci & Technol, Beijing, Peoples R China
来源
PROCEEDINGS OF 2021 INTERNATIONAL CONFERENCE ON WAVELET ANALYSIS AND PATTERN RECOGNITION (ICWAPR) | 2021年
基金
中国国家自然科学基金;
关键词
Multi-view clustering; Self-representation learning; Graph learning;
D O I
10.1109/ICWAPR54887.2021.9736151
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Multi-view clustering can mine the underlying structure of multi-view data and has attracted increasing attention. Most existing multi-view clustering methods either construct the similarity matrix from the global level through self-representation learning or construct the similarity matrix from the local level through graph learning. Spectral clustering method can be used to yield the clustering results based on the similarity matrix. However, the similarity matrix that only considers global information or local information is not robust. Moreover, separating the similarity matrix learning and clustering as two steps may lead to sub-optimal clustering results. To address these issues, we propose in this paper, a multi-view subspace clustering with local and global information (MVSCLG) method. Our method combines the self-representation learning and graph learning to learn a similarity matrix with global and local information, and simultaneously utilizes the spectral decomposition and the spectral rotation techniques to yield the clustering results. We also develop an effective optimization algorithm to solve the resulting optimization problem. The effectiveness and superiority of this method are verified on four multi-view benchmark data sets.
引用
收藏
页码:11 / 16
页数:6
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