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Unified embedding alignment with missing views inferring for incomplete multi-view clustering
Wen,Jie1; Zhang,Zheng2; Xu,Yong1; Zhang,Bob3; Fei,Lunke4; Liu,Hong5
2019-02
Conference Name33rd AAAI Conference on Artificial Intelligence
Source Publication33rd AAAI Conference on Artificial Intelligence, AAAI 2019, 31st Innovative Applications of Artificial Intelligence Conference, IAAI 2019 and the 9th AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019
Pages5393-5400
Conference DateJAN 27-FEB 01, 2019
Conference PlaceHonolulu, HI
Abstract

Multi-view clustering aims to partition data collected from diverse sources based on the assumption that all views are complete. However, such prior assumption is hardly satisfied in many real-world applications, resulting in the incomplete multi-view learning problem. The existing attempts on this problem still have the following limitations: 1) the underlying semantic information of the missing views is commonly ignored; 2) The local structure of data is not well explored; 3) The importance of different views is not effectively evaluated. To address these issues, this paper proposes a Unified Embedding Alignment Framework (UEAF) for robust incomplete multi-view clustering. In particular, a locality-preserved reconstruction term is introduced to infer the missing views such that all views can be naturally aligned. A consensus graph is adaptively learned and embedded via the reverse graph regularization to guarantee the common local structure of multiple views and in turn can further align the incomplete views and inferred views. Moreover, an adaptive weighting strategy is designed to capture the importance of different views. Extensive experimental results show that the proposed method can significantly improve the clustering performance in comparison with some state-of-the-art methods.

URLView the original
Indexed ByCPCI-S
Language英語English
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial intelligenceComputer Science, Theory & Methodsengineering, Electrical & Electronic
WOS IDWOS:000485292605052
Scopus ID2-s2.0-85065259579
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Citation statistics
Cited Times [WOS]:8   [WOS Record]     [Related Records in WOS]
Document TypeConference paper
CollectionUniversity of Macau
Corresponding AuthorWen,Jie; Zhang,Zheng; Xu,Yong; Zhang,Bob; Fei,Lunke; Liu,Hong
Affiliation1.Bio-Computing Research Center,Harbin Institute of Technology,Shenzhen, Shenzhen,China
2.University of Queensland,Australia
3.Department of Computer and Information Science,University of Macau,Taipa,Macao
4.School of Computer Science and Technology,Guangdong University of Technology,Guangzhou,China
5.Engineering Lab on Intelligent Perception for Internet of Things,Shenzhen Graduate School,Peking University,China
Corresponding Author AffilicationUniversity of Macau
Recommended Citation
GB/T 7714
Wen,Jie,Zhang,Zheng,Xu,Yong,et al. Unified embedding alignment with missing views inferring for incomplete multi-view clustering[C],2019:5393-5400.
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