CASIA OpenIR
Multiview Clustering via Unified and View-Specific Embeddings Learning
Yin, Qiyue1,2; Wu, Shu1,2; Wang, Liang1,2,3
Source PublicationIEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
ISSN2162-237X
2018-11-01
Volume29Issue:11Pages:5541-5553
Corresponding AuthorWang, Liang(wangliang@nlpr.ia.ac.cn)
AbstractMultiview 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.
KeywordIncomplete multiview data knowledge graph embedding multiview learning subspace learning
DOI10.1109/TNNLS.2017.2786743
WOS KeywordNONLINEAR DIMENSIONALITY REDUCTION ; MATRIX FACTORIZATION ; REPRESENTATION ; FRAMEWORK
Indexed BySCI
Language英语
Funding ProjectNational 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]
Funding OrganizationNational Key Research and Development Program of China ; National Natural Science Foundation of China ; Strategic Priority Research Program of the CAS ; Beijing Natural Science Foundation
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS IDWOS:000447832200029
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Citation statistics
Cited Times:7[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/26147
Collection中国科学院自动化研究所
Corresponding AuthorWang, Liang
Affiliation1.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
First Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Corresponding Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China;  Institute of Automation, Chinese Academy of Sciences
Recommended Citation
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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