Deep Embedded Multi-view Clustering with Collaborative Training
This work addresses multi-view clustering for data analysis, offering a more efficient and effective method, though it appears incremental as it builds on existing deep learning and collaborative approaches.
The paper tackles the problem of high computational complexity and limited representation capability in multi-view clustering by proposing DEMVC, which uses deep autoencoders and a collaborative training scheme to integrate consensus and complementary information across views, achieving significant improvements over state-of-the-art methods on popular datasets.
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.