CASIA OpenIR
Subspace Clustering by Integrating Sparseness and Spatial-Closeness Priors
Li,Zhe1; Pei,Haodong2,3; He,Liang1; Liu,Jiaming1; Hu,Jiaxin1; Wang,Dongji4,5
Source PublicationJournal of Physics: Conference Series
ISSN1742-6588
2020-09-01
Volume1631Issue:1
AbstractAbstract How to construct an effective sample affinity matrix is an important problem for subspace clustering, and most existing subspace clustering algorithms pursue the affinity matrix in a single space. In this paper, we propose a novel computational framework for subspace clustering, called Complementary Subspace Clustering (CSC) at first, where the affinity matrix is constructed in a pair of complementary spaces which provide different and complementary constraints on the affinity matrix. Many existing structural priors on self representation and dimensionality reduction can be seamlessly integrated into the CSC framework. Then under this framework, we explore a simple and effective subspace clustering algorithm by respectively introducing two basic priors - sparse representation and spatial closeness - into the referred pair of spaces. Moreover, a kernel variant of the proposed clustering algorithm is present. Extensive experimental results demonstrate that although only basic priors are involved, the explored algorithms from the CSC framework can improve the clustering performances significantly when the number of the sample classes is relatively big.
DOI10.1088/1742-6596/1631/1/012145
Language英语
WOS IDIOP:1742-6588-1631-1-012145
PublisherIOP Publishing
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/42196
Collection中国科学院自动化研究所
Affiliation1.Futian Power Supply Bureau, Shenzhen Power Supply Bureau Co., Ltd, Shenzhen, Guangdong 518001, China
2.Key Laboratory of Intelligent Infrared Perception, Chinese Academy of Sciences, Shanghai 200083, China
3.Shanghai Institute of Technical Physics of the Chinese Academy of Sciences, Shanghai 200083, China
4.National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
5.School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
Recommended Citation
GB/T 7714
Li,Zhe,Pei,Haodong,He,Liang,et al. Subspace Clustering by Integrating Sparseness and Spatial-Closeness Priors[J]. Journal of Physics: Conference Series,2020,1631(1).
APA Li,Zhe,Pei,Haodong,He,Liang,Liu,Jiaming,Hu,Jiaxin,&Wang,Dongji.(2020).Subspace Clustering by Integrating Sparseness and Spatial-Closeness Priors.Journal of Physics: Conference Series,1631(1).
MLA Li,Zhe,et al."Subspace Clustering by Integrating Sparseness and Spatial-Closeness Priors".Journal of Physics: Conference Series 1631.1(2020).
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Li,Zhe]'s Articles
[Pei,Haodong]'s Articles
[He,Liang]'s Articles
Baidu academic
Similar articles in Baidu academic
[Li,Zhe]'s Articles
[Pei,Haodong]'s Articles
[He,Liang]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Li,Zhe]'s Articles
[Pei,Haodong]'s Articles
[He,Liang]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.