Hyper-Laplacian Regularized Multilinear Multiview Self-Representations for Clustering and Semisupervised Learning

doi:10.1109/TCYB.2018.2869789

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

	Hyper-Laplacian Regularized Multilinear Multiview Self-Representations for Clustering and Semisupervised Learning
	Xie, Yuan1 ; Zhang, Wensheng1 ; Qu, Yanyun 2; Dai, Longquan3 ; Tao, Dacheng 4,5
发表期刊	IEEE TRANSACTIONS ON CYBERNETICS
ISSN	2168-2267
	2020-02-01
卷号	50 期号:2 页码:572-586
通讯作者	Qu, Yanyun(yyqu@xmu.edu.cn)
摘要	In this paper, we address the multiview nonlinear subspace representation problem. Traditional multiview subspace learning methods assume that the heterogeneous features of the data usually lie within the union of multiple linear subspaces. However, instead of linear subspaces, the data feature actually resides in multiple nonlinear subspaces in many real-world applications, resulting in unsatisfactory clustering performance. To overcome this, we propose a hyper-Laplacian regularized multilinear multiview self-representation model, which is referred to as HLR-(MVS)-V-2, to jointly learn multiple views correlation and a local geometrical structure in a unified tensor space and view-specific self-representation feature spaces, respectively. In unified tensor space, a well-founded tensor low-rank regularization is adopted to impose on the self-representation coefficient tensor to ensure global consensus among different views. In view-specific feature space, hypergraph-induced hyper-Laplacian regularization is utilized to preserve the local geometrical structure embedded in a high-dimensional ambient space. An efficient algorithm is then derived to solve the optimization problem of the established model with theoretical convergence guarantee. Furthermore, the proposed model can be extended to semisupervised classification without introducing any additional parameters. An extensive experiment of our method is conducted on many challenging datasets, where a clear advance over state-of-the-art multiview clustering and multiview semisupervised classification approaches is achieved.
关键词	Tensile stress Manifolds Encoding Laplace equations Semisupervised learning Correlation Optimization Manifold regularization multilinear multiview features nonlinear subspace clustering tensor singular-value decomposition (t-SVD)
DOI	10.1109/TCYB.2018.2869789
关键词[WOS]	SPARSE ; CLASSIFICATION ; RECOGNITION ; FRAMEWORK ; ALGORITHM ; SCENE ; GRAPH
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[61772524] ; National Natural Science Foundation of China[61772525] ; National Natural Science Foundation of China[61876161] ; National Natural Science Foundation of China[61701235] ; National Natural Science Foundation of China[61373077] ; National Natural Science Foundation of China[61602482] ; Beijing Municipal Natural Science Foundation[4182067] ; Fundamental Research Funds for the Central Universities[30917011323] ; Australian Research Council[FT-130101457] ; Australian Research Council[DP-120103730] ; Australian Research Council[LP-150100671] ; National Natural Science Foundation of China[61772524] ; National Natural Science Foundation of China[61772525] ; National Natural Science Foundation of China[61876161] ; National Natural Science Foundation of China[61701235] ; National Natural Science Foundation of China[61373077] ; National Natural Science Foundation of China[61602482] ; Beijing Municipal Natural Science Foundation[4182067] ; Fundamental Research Funds for the Central Universities[30917011323] ; Australian Research Council[FT-130101457] ; Australian Research Council[DP-120103730] ; Australian Research Council[LP-150100671]
项目资助者	National Natural Science Foundation of China ; Beijing Municipal Natural Science Foundation ; Fundamental Research Funds for the Central Universities ; Australian Research Council
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:000506849800015
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：105[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/173211/29494
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	Qu, Yanyun
作者单位	1.Chinese Acad Sci, Inst Automat, Res Ctr Precis Sensing & Control, Beijing 100190, Peoples R China 2.Xiamen Univ, Sch Informat Sci & Technol, Xiamen 361005, Peoples R China 3.Nanjing Univ Sci & Technol, Sch Comp Sci & Engn, Nanjing 210094, Peoples R China 4.Univ Sydney, UBTECH Sydney Artificial Intelligence Ctr, Darlington, NSW 2008, Australia 5.Univ Sydney, Fac Engn & Informat Technol, Sch Informat Technol, Darlington, NSW 2008, Australia
第一作者单位	精密感知与控制研究中心
推荐引用方式 GB/T 7714	Xie, Yuan,Zhang, Wensheng,Qu, Yanyun,et al. Hyper-Laplacian Regularized Multilinear Multiview Self-Representations for Clustering and Semisupervised Learning[J]. IEEE TRANSACTIONS ON CYBERNETICS,2020,50(2):572-586.
APA	Xie, Yuan,Zhang, Wensheng,Qu, Yanyun,Dai, Longquan,&Tao, Dacheng.(2020).Hyper-Laplacian Regularized Multilinear Multiview Self-Representations for Clustering and Semisupervised Learning.IEEE TRANSACTIONS ON CYBERNETICS,50(2),572-586.
MLA	Xie, Yuan,et al."Hyper-Laplacian Regularized Multilinear Multiview Self-Representations for Clustering and Semisupervised Learning".IEEE TRANSACTIONS ON CYBERNETICS 50.2(2020):572-586.