Multiview Clustering via Unified and View-Specific Embeddings Learning | |
Yin, Qiyue1,2![]() ![]() ![]() | |
发表期刊 | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
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ISSN | 2162-237X |
2018-11-01 | |
卷号 | 29期号:11页码:5541-5553 |
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. |
关键词 | Incomplete multiview data knowledge graph embedding multiview learning subspace learning |
DOI | 10.1109/TNNLS.2017.2786743 |
关键词[WOS] | NONLINEAR DIMENSIONALITY REDUCTION ; MATRIX FACTORIZATION ; REPRESENTATION ; FRAMEWORK |
收录类别 | SCI |
语种 | 英语 |
资助项目 | Strategic Priority Research Program of the CAS[XDB02070001] ; National Natural Science Foundation of China[61572504] ; National Natural Science Foundation of China[61772528] ; Beijing Natural Science Foundation[4162058] ; National Natural Science Foundation of China[61420106015] ; National Natural Science Foundation of China[61633021] ; National Natural Science Foundation of China[61525306] ; National Key Research and Development Program of China[2016YFB1001000] ; National Key Research and Development Program of China[2016YFB1001000] ; National Natural Science Foundation of China[61525306] ; National Natural Science Foundation of China[61633021] ; National Natural Science Foundation of China[61420106015] ; Beijing Natural Science Foundation[4162058] ; National Natural Science Foundation of China[61772528] ; National Natural Science Foundation of China[61572504] ; Strategic Priority Research Program of the CAS[XDB02070001] |
WOS研究方向 | Computer Science ; Engineering |
WOS类目 | Computer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic |
WOS记录号 | WOS:000447832200029 |
出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
七大方向——子方向分类 | 机器学习 |
国重实验室规划方向分类 | 智能计算与学习 |
是否有论文关联数据集需要存交 | 否 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/26147 |
专题 | 模式识别实验室 |
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 |
第一作者单位 | 模式识别国家重点实验室 |
通讯作者单位 | 模式识别国家重点实验室; 中国科学院自动化研究所 |
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. |
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