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- Publisher Website: 10.1109/TIP.2018.2877937
- Scopus: eid_2-s2.0-85055726188
- WOS: WOS:000458850800001
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Article: Tensor-Based Low-Dimensional Representation Learning for Multi-View Clustering
Title | Tensor-Based Low-Dimensional Representation Learning for Multi-View Clustering |
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Authors | |
Keywords | representation learning Multi-view clustering third-order tensor analysis tensor decomposition |
Issue Date | 2019 |
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 Identifier | http://hdl.handle.net/10722/276612 |
ISSN | 2021 Impact Factor: 11.041 2020 SCImago Journal Rankings: 1.778 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Cheng, Miaomiao | - |
dc.contributor.author | Jing, Liping | - |
dc.contributor.author | Ng, Michael K. | - |
dc.date.accessioned | 2019-09-18T08:34:08Z | - |
dc.date.available | 2019-09-18T08:34:08Z | - |
dc.date.issued | 2019 | - |
dc.identifier.citation | IEEE Transactions on Image Processing, 2019, v. 28, n. 5, p. 2399-2414 | - |
dc.identifier.issn | 1057-7149 | - |
dc.identifier.uri | http://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.language | eng | - |
dc.relation.ispartof | IEEE Transactions on Image Processing | - |
dc.subject | representation learning | - |
dc.subject | Multi-view clustering | - |
dc.subject | third-order tensor analysis | - |
dc.subject | tensor decomposition | - |
dc.title | Tensor-Based Low-Dimensional Representation Learning for Multi-View Clustering | - |
dc.type | Article | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1109/TIP.2018.2877937 | - |
dc.identifier.scopus | eid_2-s2.0-85055726188 | - |
dc.identifier.volume | 28 | - |
dc.identifier.issue | 5 | - |
dc.identifier.spage | 2399 | - |
dc.identifier.epage | 2414 | - |
dc.identifier.isi | WOS:000458850800001 | - |
dc.identifier.issnl | 1057-7149 | - |