On Unifying Multi-view Self-Representations for Clustering by Tensor Multi-rank Minimization

doi:10.1007/s11263-018-1086-2

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

	On Unifying Multi-view Self-Representations for Clustering by Tensor Multi-rank Minimization
	Xie, Yuan1 ; Tao, Dacheng 2; Zhang, Wensheng1 ; Liu, Yan 3; Zhang, Lei 3; Qu, Yanyun 4
发表期刊	INTERNATIONAL JOURNAL OF COMPUTER VISION
ISSN	0920-5691
	2018-11-01
卷号	126 期号:11 页码:1157-1179
文章类型	Article
摘要	In this paper, we address the multi-view subspace clustering problem. Our method utilizes the circulant algebra for tensor, which is constructed by stacking the subspace representation matrices of different views and then rotating, to capture the low rank tensor subspace so that the refinement of the view-specific subspaces can be achieved, as well as the high order correlations underlying multi-view data can be explored. By introducing a recently proposed tensor factorization, namely tensor-Singular Value Decomposition (t-SVD) (Kilmer et al. in SIAM J Matrix Anal Appl 34(1):148-172, 2013), we can impose a new type of low-rank tensor constraint on the rotated tensor to ensure the consensus among multiple views. Different from traditional unfolding based tensor norm, this low-rank tensor constraint has optimality properties similar to that of matrix rank derived from SVD, so the complementary information can be explored and propagated among all the views more thoroughly and effectively. The established model, called t-SVD based Multi-view Subspace Clustering (t-SVD-MSC), falls into the applicable scope of augmented Lagrangian method, and its minimization problem can be efficiently solved with theoretical convergence guarantee and relatively low computational complexity. Extensive experimental testing on eight challenging image datasets shows that the proposed method has achieved highly competent objective performance compared to several state-of-the-art multi-view clustering methods.
关键词	T-svd Tensor Multi-rank Multi-view Features Subspace Clustering
WOS标题词	Science & Technology ; Technology
DOI	10.1007/s11263-018-1086-2
关键词[WOS]	3RD-ORDER TENSORS ; CATEGORIES ; ALGORITHM ; OPERATORS ; SCENE
收录类别	SCI
语种	英语
项目资助者	National Natural Science Foundation of China(61772524 ; Beijing Natural Science Foundation(4182067) ; Australian Research Council(FL-170100117 ; HK RGC General Research Fund(PolyU 152135/16E) ; 61402480 ; DP-180103424 ; 61373077 ; DP-140102164 ; 61602482) ; LP-150100671)
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence
WOS记录号	WOS:000444394200001
出版者	SPRINGER
引用统计	被引频次：250[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/173211/27919
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
通讯作者	Xie, Yuan
作者单位	1.Chinese Acad Sci, Inst Automat, Res Ctr Precis Sensing & Control, Beijing 100190, Peoples R China 2.Univ Sydney, Fac Engn & Informat Technol, Sch Informat Technol, UBTECH Sydney Artificial Intelligence Ctr, 6 Cleveland St, Darlington, NSW 2008, Australia 3.Hong Kong Polytech Univ, Dept Comp, Hong Kong, Hong Kong, Peoples R China 4.Xiamen Univ, Sch Informat Sci & Technol, Xiamen, Fujian, 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	Xie, Yuan,Tao, Dacheng,Zhang, Wensheng,et al. On Unifying Multi-view Self-Representations for Clustering by Tensor Multi-rank Minimization[J]. INTERNATIONAL JOURNAL OF COMPUTER VISION,2018,126(11):1157-1179.
APA	Xie, Yuan,Tao, Dacheng,Zhang, Wensheng,Liu, Yan,Zhang, Lei,&Qu, Yanyun.(2018).On Unifying Multi-view Self-Representations for Clustering by Tensor Multi-rank Minimization.INTERNATIONAL JOURNAL OF COMPUTER VISION,126(11),1157-1179.
MLA	Xie, Yuan,et al."On Unifying Multi-view Self-Representations for Clustering by Tensor Multi-rank Minimization".INTERNATIONAL JOURNAL OF COMPUTER VISION 126.11(2018):1157-1179.