Multiview Clustering via Unified and View-Specific Embeddings Learning
文献类型：期刊论文
| 作者 | Yin, Qiyue1,2 ; Wu, Shu1,2; Wang, Liang1,2,3
|
| 刊名 | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
 |
| 出版日期 | 2018-11-01 |
| 卷号 | 29期号:11页码:5541-5553 |
| 关键词 | Incomplete multiview data knowledge graph embedding multiview learning subspace learning |
| ISSN号 | 2162-237X |
| DOI | 10.1109/TNNLS.2017.2786743 |
| 通讯作者 | Wang, Liang(wangliang@nlpr.ia.ac.cn) |
| 英文摘要 | Multiview clustering, which aims at using multiple distinct feature sets to boost clustering performance, has a wide range of applications. A subspace-based approach, a type of widely used methods, learns unified embedding from multiple sources of information and gives a relatively good performance. However, these methods usually ignore data similarity rankings; for example, example A may be more similar to B than C, and such similarity triplets may be more effective in revealing the data cluster structure. Motivated by recent embedding methods for modeling knowledge graph in natural-language processing, this paper proposes to mimic different views as different relations in a knowledge graph for unified and view-specific embedding learning. Moreover, in real applications, it happens so often that some views suffer from missing information, leading to incomplete multiview data. Under such a scenario, the performance of conventional multiview clustering degenerates notably, whereas the method we propose here can be naturally extended for incomplete multiview clustering, which enables full use of examples with incomplete feature sets for model promotion. Finally, we demonstrate through extensive experiments that our method performs better than the state-of-the-art clustering methods. |
| WOS关键词 | NONLINEAR DIMENSIONALITY REDUCTION ; MATRIX FACTORIZATION ; REPRESENTATION ; FRAMEWORK |
| 资助项目 | National Key Research and Development Program of China[2016YFB1001000] ; National Natural Science Foundation of China[61772528] ; National Natural Science Foundation of China[61525306] ; National Natural Science Foundation of China[61633021] ; National Natural Science Foundation of China[61572504] ; National Natural Science Foundation of China[61420106015] ; Strategic Priority Research Program of the CAS[XDB02070001] ; Beijing Natural Science Foundation[4162058] |
| WOS研究方向 | Computer Science ; Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:000447832200029 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 资助机构 | National Key Research and Development Program of China ; National Natural Science Foundation of China ; Strategic Priority Research Program of the CAS ; Beijing Natural Science Foundation |
| 源URL | [http://ir.ia.ac.cn/handle/173211/26147]  |
| 专题 | 自动化研究所_智能感知与计算研究中心 |
| 通讯作者 | Wang, Liang |
| 作者单位 | 1.Chinese Acad Sci, Ctr Res Intelligent Percept & Comp, Natl Lab Pattern Recognit, Inst Automat, Beijing, Peoples R China 2.Univ Chinese Acad Sci, Beijing, Peoples R China 3.Chinese Acad Sci, Inst Automat, Ctr Excellence Brain Sci & Intelligence Technol, Beijing, Peoples R China |
| 推荐引用方式 GB/T 7714 | Yin, Qiyue,Wu, Shu,Wang, Liang. Multiview Clustering via Unified and View-Specific Embeddings Learning[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2018,29(11):5541-5553. |
| APA | Yin, Qiyue,Wu, Shu,&Wang, Liang.(2018).Multiview Clustering via Unified and View-Specific Embeddings Learning.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,29(11),5541-5553. |
| MLA | Yin, Qiyue,et al."Multiview Clustering via Unified and View-Specific Embeddings Learning".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 29.11(2018):5541-5553. |
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。