Subspace Clustering by Integrating Sparseness and Spatial-Closeness Priors

doi:10.1088/1742-6596/1631/1/012145

CASIA OpenIR

	Subspace Clustering by Integrating Sparseness and Spatial-Closeness Priors
	Li,Zhe1 ; Pei,Haodong 2,3; He,Liang 1; Liu,Jiaming 1; Hu,Jiaxin 1; Wang,Dongji 4,5
Source Publication	Journal of Physics: Conference Series
ISSN	1742-6588
	2020-09-01
Volume	1631 Issue:1
Abstract	Abstract 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.
DOI	10.1088/1742-6596/1631/1/012145
Language	英语
WOS ID	IOP:1742-6588-1631-1-012145
Publisher	IOP Publishing
Citation statistics
Document Type	期刊论文
Identifier	http://ir.ia.ac.cn/handle/173211/42196
Collection	中国科学院自动化研究所
Affiliation	1.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).

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