CASIA OpenIR  > 精密感知与控制研究中心  > 人工智能与机器学习
One-Step Multiview Subspace Segmentation via Joint Skinny Tensor Learning and Latent Clustering
Tang, Yongqiang1; Xie, Yuan2; Zhang, Changqing3; Zhang, Zhizhong2; Zhang, Wensheng1,4
Source PublicationIEEE TRANSACTIONS ON CYBERNETICS
ISSN2168-2267
2021-03-04
Pages15
Corresponding AuthorZhang, Wensheng(zhangwenshengia@hotmail.com)
AbstractMultiview subspace clustering (MSC) has attracted growing attention due to the extensive value in various applications, such as natural language processing, face recognition, and time-series analysis. In this article, we are devoted to address two crucial issues in MSC: 1) high computational cost and 2) cumbersome multistage clustering. Existing MSC approaches, including tensor singular value decomposition (t-SVD)-MSC that has achieved promising performance, generally utilize the dataset itself as the dictionary and regard representation learning and clustering process as two separate parts, thus leading to the high computational overhead and unsatisfactory clustering performance. To remedy these two issues, we propose a novel MSC model called joint skinny tensor learning and latent clustering (JSTC), which can learn high-order skinny tensor representations and corresponding latent clustering assignments simultaneously. Through such a joint optimization strategy, the multiview complementary information and latent clustering structure can be exploited thoroughly to improve the clustering performance. An alternating direction minimization algorithm, which owns low computational complexity and can be run in parallel when solving several key subproblems, is carefully designed to optimize the JSTC model. Such a nice property makes our JSTC an appealing solution for large-scale MSC problems. We conduct extensive experiments on ten popular datasets and compare our JSTC with 12 competitors. Five commonly used metrics, including four external measures (NMI, ACC, F-score, and RI) and one internal metric (SI), are adopted to evaluate the clustering quality. The experimental results with the Wilcoxon statistical test demonstrate the superiority of the proposed method in both clustering performance and operational efficiency.
KeywordTensors Optimization Clustering methods Computational modeling Clustering algorithms Semantics Matrix decomposition Multiview clustering multiview representations subspace clustering tensor singular value decomposition (t-SVD)
DOI10.1109/TCYB.2021.3053057
WOS KeywordALGORITHM
Indexed BySCI
Language英语
Funding ProjectKey-Area Research and Development Program of Guangdong Province[2019B010153002] ; National Natural Science Foundation of China[U1936206] ; National Natural Science Foundation of China[61976212] ; National Natural Science Foundation of China[61906190] ; National Natural Science Foundation of China[61876183] ; National Natural Science Foundation of China[61772524] ; Natural Science Foundation of Shanghai[20ZR1417700] ; Research Program of Zhejiang Lab[2019KD0AC02]
Funding OrganizationKey-Area Research and Development Program of Guangdong Province ; National Natural Science Foundation of China ; Natural Science Foundation of Shanghai ; Research Program of Zhejiang Lab
WOS Research AreaAutomation & Control Systems ; Computer Science
WOS SubjectAutomation & Control Systems ; Computer Science, Artificial Intelligence ; Computer Science, Cybernetics
WOS IDWOS:000732252800001
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Citation statistics
Cited Times:1[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/47005
Collection精密感知与控制研究中心_人工智能与机器学习
精密感知与控制研究中心
Corresponding AuthorZhang, Wensheng
Affiliation1.Chinese Acad Sci, Res Ctr Precis Sensing & Control, Inst Automat, Beijing 100190, Peoples R China
2.East China Normal Univ, Sch Comp Sci & Technol, Shanghai 200241, Peoples R China
3.Tianjin Univ, Sch Comp Sci & Technol, Tianjin 300072, Peoples R China
4.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 101408, Peoples R China
First Author AffilicationChinese Acad Sci, Inst Automat, Res Ctr Precis Sensing & Control, Beijing 100190, Peoples R China
Corresponding Author AffilicationChinese Acad Sci, Inst Automat, Res Ctr Precis Sensing & Control, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Tang, Yongqiang,Xie, Yuan,Zhang, Changqing,et al. One-Step Multiview Subspace Segmentation via Joint Skinny Tensor Learning and Latent Clustering[J]. IEEE TRANSACTIONS ON CYBERNETICS,2021:15.
APA Tang, Yongqiang,Xie, Yuan,Zhang, Changqing,Zhang, Zhizhong,&Zhang, Wensheng.(2021).One-Step Multiview Subspace Segmentation via Joint Skinny Tensor Learning and Latent Clustering.IEEE TRANSACTIONS ON CYBERNETICS,15.
MLA Tang, Yongqiang,et al."One-Step Multiview Subspace Segmentation via Joint Skinny Tensor Learning and Latent Clustering".IEEE TRANSACTIONS ON CYBERNETICS (2021):15.
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
[Tang, Yongqiang]'s Articles
[Xie, Yuan]'s Articles
[Zhang, Changqing]'s Articles
Baidu academic
Similar articles in Baidu academic
[Tang, Yongqiang]'s Articles
[Xie, Yuan]'s Articles
[Zhang, Changqing]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Tang, Yongqiang]'s Articles
[Xie, Yuan]'s Articles
[Zhang, Changqing]'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.