One-Step Multiview Subspace Segmentation via Joint Skinny Tensor Learning and Latent Clustering

doi:10.1109/TCYB.2021.3053057

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 人工智能与机器学习（杨雪冰）-技术团队

	One-Step Multiview Subspace Segmentation via Joint Skinny Tensor Learning and Latent Clustering
	Tang, Yongqiang1 ; Xie, Yuan2 ; Zhang, Changqing 3; Zhang, Zhizhong2 ; Zhang, Wensheng1,4
发表期刊	IEEE TRANSACTIONS ON CYBERNETICS
ISSN	2168-2267
	2021-03-04
页码	15
通讯作者	Zhang, Wensheng(zhangwenshengia@hotmail.com)
摘要	Multiview 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.
关键词	Tensors Optimization Clustering methods Computational modeling Clustering algorithms Semantics Matrix decomposition Multiview clustering multiview representations subspace clustering tensor singular value decomposition (t-SVD)
DOI	10.1109/TCYB.2021.3053057
关键词[WOS]	ALGORITHM
收录类别	SCI
语种	英语
资助项目	Key-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]
项目资助者	Key-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研究方向	Automation & Control Systems ; Computer Science
WOS类目	Automation & Control Systems ; Computer Science, Artificial Intelligence ; Computer Science, Cybernetics
WOS记录号	WOS:000732252800001
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
七大方向——子方向分类	机器学习
引用统计	被引频次：22[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/47005
专题	多模态人工智能系统全国重点实验室_人工智能与机器学习（杨雪冰）-技术团队精密感知与控制研究中心
通讯作者	Zhang, Wensheng
作者单位	1.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
第一作者单位	精密感知与控制研究中心
通讯作者单位	精密感知与控制研究中心
推荐引用方式 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.