Automatic and Aligned Anchor Learning Strategy for Multi-View Clustering

Multi-View Clustering (MVC) commonly utilizes the anchor technique to mitigate the computational complexity. Existing methods generally assume a pre-selection of anchors to facilitate subsequent clustering tasks. However, the determination of the optimal number of anchors is often non-trivial and necessitates their treatment as a tunable parameter, incurring additional computational overhead. Moreover, it is not reasonable to assume an identical number of anchors across all views, as this assumption restricts the representational capacity of anchors in individual views. To address the above issues, we propose a view adaptive anchor multi-view clustering called Multi-view Clustering with Automatic and Aligned Anchor (3AMVC). We introduce a Hierarchical Bipartite Neighbor Clustering (HBNC) strategy to adaptively select a suitable number of representative anchors in each view. Specifically, when the representative difference of anchors lies in a acceptable and satisfactory range, the HBNC process is halted and picks out the final anchors. Moreover, we propose an innovative anchor alignment strategy in response to the varying quantities of anchors across different views. This approach initially evaluates the quality of anchors on each view based on the intra-cluster distance criterion and then proceeds to align based on the view with the highest-quality anchors. The carefully organized experiments well validate the effectiveness and strengthens of 3AMVC.