Hyper-Laplacian Regularized Multilinear Multiview Self-Representations for Clustering and Semisupervised Learning
文献类型：期刊论文
| 作者 | Xie, Yuan3 ; Zhang, Wensheng3; Qu, Yanyun2; Dai, Longquan1; Tao, Dacheng4,5
|
| 刊名 | IEEE TRANSACTIONS ON CYBERNETICS
 |
| 出版日期 | 2020-02-01 |
| 卷号 | 50期号:2页码:572-586 |
| 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) |
| ISSN号 | 2168-2267 |
| DOI | 10.1109/TCYB.2018.2869789 |
| 通讯作者 | Qu, Yanyun(yyqu@xmu.edu.cn) |
| 英文摘要 | 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. |
| WOS关键词 | SPARSE ; CLASSIFICATION ; RECOGNITION ; FRAMEWORK ; ALGORITHM ; SCENE ; GRAPH |
| 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] |
| WOS研究方向 | Automation & Control Systems ; Computer Science |
| 语种 | 英语 |
| WOS记录号 | WOS:000506849800015 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 资助机构 | National Natural Science Foundation of China ; Beijing Municipal Natural Science Foundation ; Fundamental Research Funds for the Central Universities ; Australian Research Council |
| 源URL | [http://ir.ia.ac.cn/handle/173211/29493]  |
| 专题 | 精密感知与控制研究中心_人工智能与机器学习 |
| 通讯作者 | Qu, Yanyun |
| 作者单位 | 1.Nanjing Univ Sci & Technol, Sch Comp Sci & Engn, Nanjing 210094, Peoples R China 2.Xiamen Univ, Sch Informat Sci & Technol, Xiamen 361005, Peoples R China 3.Chinese Acad Sci, Inst Automat, Res Ctr Precis Sensing & Control, Beijing 100190, Peoples R China 4.Univ Sydney, Fac Engn & Informat Technol, Sch Informat Technol, Darlington, NSW 2008, Australia 5.Univ Sydney, UBTECH Sydney Artificial Intelligence Ctr, Darlington, NSW 2008, Australia |
| 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. |
入库方式： OAI收割
来源：自动化研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。