The DOI matches its Crossref record; 38 of the 38 references it cites were found in a registry or an index.
Deep embedded multi-view clustering with collaborative training
Open accessAbstract
Multi-view clustering has attracted increasing attentions recently by utilizing information from multiple views. However, existing multi-view clustering methods are either with high computation and space complexities, or lack of representation capability. To address these issues, we propose deep embedded multi-view clustering with collaborative training (DEMVC) in this paper. Firstly, the embedded representations of multiple views are learned individually by deep autoencoders. Then, both consensus and complementary of multiple views are taken into account and a novel collaborative training scheme is proposed. Concretely, the feature representations and cluster assignments of all views are learned collaboratively. A new consistency strategy for cluster centers initialization is further developed to improve the multi-view clustering performance with collaborative training. Experimental results on several popular multi-view datasets show that DEMVC achieves significant improvements over state-of-the-art methods.
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Cited by
Elastic deep multi-view autoencoder with diversity embedding
Daneshfar, F., Saifee, B. S., Soleymanbaigi, S., & Aeini, M. (2025) · Information Sciences · doi:10.1016/j.ins.2024.121482 · Cited by 55
Metadata
- Authors
- Xu, J., Ren, Y., Li, G., Pan, L., Zhu, C., & Xu, Z.
- Venue
- Information Sciences
- Publisher
- Elsevier BV
- Published
- 2021-09
- Volume
- 573
- Pages
- 279-290
- ISSN
- 0020-0255 (ISSN-L)
- OpenAlex
- W3044096495
- Cited by
- 269
- References
- 38
- FWCI
- 12.44
- Type
- journal article
- Language
- en
- Open access
- green
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