Density Peak Anchor Selection for Multi-View Clustering

Multi-view clustering (MVC) typically enhances clustering performance by leveraging consistent and complementary information across views. With increasing data scale, many MVC methods employ few anchors to represent data, effectively reducing complexity. Traditional anchor selection methods include random sampling andk-means. Recent methods leverage variance or inter-class error to guide anchor selection. However, their anchors are selected sequentially, where each newly selected anchor focuses on divergences from the previous anchor. This local perspective leads to unstable anchor coverage and fails to fully represent complex cluster shapes. An anchor selection algorithm, termed DPAS, is proposed to solve this problem. From a novel density perspective, it selects density peaks satisfying criteria as anchors and reduces complexity based on granular balls. As a result, the efficient DPAS exhibits stronger representativeness and robustness. Furthermore, the order and number of anchors selected from different views typically need to be aligned. The assumptions of existing alignment methods are often too restrictive, compromising the quality of alignment. Thus, an alignment method FAA based on flexible assumptions is designed to improve generalization. In this paper, we propose a new fast MVC method without alternating optimization named DPAS-MC. First, DPAS is applied to select high-quality anchors for views. Then, anchor graphs are computed, aligned, and fused via FAA. Finally, a low-rank tensor is calculated to obtain clustering results. Experiments show that our methods outperform existing state-of-the-art methods in performance and efficiency. The code is available at https://github.com/ytccyw.