@inproceedings{
ma2024automatic,
title={Automatic and Aligned Anchor Learning Strategy for Multi-View Clustering},
author={Huimin Ma and Siwei Wang and Shengju Yu and Suyuan Liu and Jun-Jie Huang and Huijun Wu and Xinwang Liu and En Zhu},
booktitle={ACM Multimedia 2024},
year={2024},
url={https://openreview.net/forum?id=TKRqWQVawP}
}
Multi-view Clustering (MVC) generally utilizes the anchor technique to decrease the computational complexity so as to tackle large-scale scenarios.  Existing researches generally are supposed to select anchors in advance to complete the next clustering task. Nevertheless, the number of anchors cannot be predetermined and must be selected as a parameter, which introduces additional time consumption for parameter search. Moreover, maintaining an identical number of anchors across each view is not reasonable, as it 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 the Hierarchical Bipartite Neighbor Clustering (HBNC) strategy to adaptively select a suitable number of representative anchors from the original samples of 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. In addition, in response to the varying quantities of anchors across different views, we propose an innovative anchor alignment strategy. 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.
Automatic and Aligned Anchor Learning Strategy for Multi-View Clustering
Huimin Ma Siwei Wang Shengju Yu Suyuan Liu Jun-Jie Huang Huijun Wu Xinwang Liu En Zhu