A unified framework for multi-view spectral clustering
Zhong,Guo; Pun,Chi Man
Source PublicationProceedings - International Conference on Data Engineering
AbstractIn the era of big data, multi-view clustering has drawn considerable attention in machine learning and data mining communities due to the existence of a large number of unlabeled multi-view data in reality. Traditional spectral graph theoretic methods have recently been extended to multi-view clustering and shown outstanding performance. However, most of them still consist of two separate stages: learning a fixed common real matrix (i.e., continuous labels) of all the views from original data, and then applying K-means to the resulting common label matrix to obtain the final clustering results. To address these, we design a unified multi-view spectral clustering scheme to learn the discrete cluster indicator matrix in one stage. Specifically, the proposed framework directly obtain clustering results without performing K-means clustering. Experimental results on several famous benchmark datasets verify the effectiveness and superiority of the proposed method compared to the state-of-the-arts.
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Scopus ID2-s2.0-85085855948
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Cited Times [WOS]:1   [WOS Record]     [Related Records in WOS]
Document TypeConference paper
CollectionUniversity of Macau
AffiliationUniversity of Macau,Department of Computer and Information Science,Macao
First Author AffilicationUniversity of Macau
Recommended Citation
GB/T 7714
Zhong,Guo,Pun,Chi Man. A unified framework for multi-view spectral clustering[C],2020:1854-1857.
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