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Article: Tensor-Based Low-Dimensional Representation Learning for Multi-View Clustering

TitleTensor-Based Low-Dimensional Representation Learning for Multi-View Clustering
Authors
Keywordsrepresentation learning
Multi-view clustering
third-order tensor analysis
tensor decomposition
Issue Date2019
Citation
IEEE Transactions on Image Processing, 2019, v. 28, n. 5, p. 2399-2414 How to Cite?
Abstract© 2019 IEEE. With the development of data collection techniques, multi-view clustering becomes an emerging research direction to improve the clustering performance. This paper has shown that leveraging multi-view information is able to provide a rich and comprehensive description. One of the core problems is how to sufficiently represent multi-view data in the analysis. In this paper, we introduce a tensor-based representation learning method for multi-view clustering (tRLMvC) that can unify heterogeneous and high-dimensional multi-view feature spaces to a low-dimensional shared latent feature space and improve multi-view clustering performance. To sufficiently capture plenty multi-view information, the tRLMvC represents multi-view data as a third-order tensor, expresses each tensorial data point as a sparse t -linear combination of all data points with t -product, and constructs a self-expressive tensor through reconstruction coefficients. The low-dimensional multi-view data representation in the shared latent feature space can be obtained via Tucker decomposition on the self-expressive tensor. These two parts are iteratively performed so that the interaction between self-expressive tensor learning and its factorization can be enhanced and the new representation can be effectively generated for clustering purpose. We conduct extensive experiments on eight multi-view data sets and compare the proposed model with the state-of-the-art methods. Experimental results have shown that tRLMvC outperforms the baselines in terms of various evaluation metrics.
Persistent Identifierhttp://hdl.handle.net/10722/276612
ISSN
2020 Impact Factor: 10.856
2020 SCImago Journal Rankings: 1.778
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorCheng, Miaomiao-
dc.contributor.authorJing, Liping-
dc.contributor.authorNg, Michael K.-
dc.date.accessioned2019-09-18T08:34:08Z-
dc.date.available2019-09-18T08:34:08Z-
dc.date.issued2019-
dc.identifier.citationIEEE Transactions on Image Processing, 2019, v. 28, n. 5, p. 2399-2414-
dc.identifier.issn1057-7149-
dc.identifier.urihttp://hdl.handle.net/10722/276612-
dc.description.abstract© 2019 IEEE. With the development of data collection techniques, multi-view clustering becomes an emerging research direction to improve the clustering performance. This paper has shown that leveraging multi-view information is able to provide a rich and comprehensive description. One of the core problems is how to sufficiently represent multi-view data in the analysis. In this paper, we introduce a tensor-based representation learning method for multi-view clustering (tRLMvC) that can unify heterogeneous and high-dimensional multi-view feature spaces to a low-dimensional shared latent feature space and improve multi-view clustering performance. To sufficiently capture plenty multi-view information, the tRLMvC represents multi-view data as a third-order tensor, expresses each tensorial data point as a sparse t -linear combination of all data points with t -product, and constructs a self-expressive tensor through reconstruction coefficients. The low-dimensional multi-view data representation in the shared latent feature space can be obtained via Tucker decomposition on the self-expressive tensor. These two parts are iteratively performed so that the interaction between self-expressive tensor learning and its factorization can be enhanced and the new representation can be effectively generated for clustering purpose. We conduct extensive experiments on eight multi-view data sets and compare the proposed model with the state-of-the-art methods. Experimental results have shown that tRLMvC outperforms the baselines in terms of various evaluation metrics.-
dc.languageeng-
dc.relation.ispartofIEEE Transactions on Image Processing-
dc.subjectrepresentation learning-
dc.subjectMulti-view clustering-
dc.subjectthird-order tensor analysis-
dc.subjecttensor decomposition-
dc.titleTensor-Based Low-Dimensional Representation Learning for Multi-View Clustering-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1109/TIP.2018.2877937-
dc.identifier.scopuseid_2-s2.0-85055726188-
dc.identifier.volume28-
dc.identifier.issue5-
dc.identifier.spage2399-
dc.identifier.epage2414-
dc.identifier.isiWOS:000458850800001-
dc.identifier.issnl1057-7149-

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