Learn from View Correlation: An Anchor Enhancement Strategy for Multi-view Clustering

Suyuan Liu, Ke Liang, Zhibin Dong, Siwei Wang, Xihong Yang, Sihang Zhou, En Zhu, Xinwang Liu; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024, pp. 26151-26161

In recent years anchor-based methods have achieved promising progress in multi-view clustering. The performances of these methods are significantly affected by the quality of the anchors. However the anchors generated by previous works solely rely on single-view information ignoring the correlation among different views. In particular we observe that similar patterns are more likely to exist between similar views so such correlation information can be leveraged to enhance the quality of the anchors which is also omitted. To this end we propose a novel plug-and-play anchor enhancement strategy through view correlation for multi-view clustering. Specifically we construct a view graph based on aligned initial anchor graphs to explore inter-view correlations. By learning from view correlation we enhance the anchors of the current view using the relationships between anchors and samples on neighboring views thereby narrowing the spatial distribution of anchors on similar views. Experimental results on seven datasets demonstrate the superiority of our proposed method over other existing methods. Furthermore extensive comparative experiments validate the effectiveness of the proposed anchor enhancement module when applied to various anchor-based methods.