Deep Embedded Multi-view Clustering with Collaborative Training
- URL: http://arxiv.org/abs/2007.13067v1
- Date: Sun, 26 Jul 2020 06:51:57 GMT
- Title: Deep Embedded Multi-view Clustering with Collaborative Training
- Authors: Jie Xu, Yazhou Ren, Guofeng Li, Lili Pan, Ce Zhu, Zenglin Xu
- Abstract summary: Multi-view clustering has attracted increasing attentions recently by utilizing information from multiple views.
Existing multi-view clustering methods are either with high computation and space complexities, or lack of representation capability.
We propose deep embedded multi-view clustering with collaborative training (DEMVC) in this paper.
- Score: 42.289184796907655
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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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