Anchor point segmentation based multi-view clustering.

Abstract

Existing bipartite graph based methods commonly learn a consistent anchor graph across multiple views utilizing various optimization techniques to determine clustering assignments, maintaining linear complexity w.r.t. the number of samples. Owing to their efficiency and effectiveness, these approaches have attracted significant attention. However, the inherent geometric relationship in which anchors and the raw data share common centroids remains under-explored, leaving room for potential improvements in algorithm efficiency. This relationship enables the use of anchors to efficiently learn clustering centroids. In this paper, we propose a novel multi-view clustering approach termed anchor point segmentation based multi-view clustering (APS-MVC). Specifically, we group the raw data by first assigning each data point to an anchor point, then to a centroid. This process is modeled as a two-step transition within a Markov chain, where the optimal centroids and the soft partition of anchors are learned simultaneously by encoding the graph structure information of the anchor points. Furthermore, the proposed APS-MVC effectively tackles the out-of-sample issue. The resultant optimization problem is solved efficiently, exhibiting square complexity w.r.t. the number of anchors. Experimental results on six benchmark datasets validate the effectiveness of the proposed method. The source code is available at: https://github.com/Wenhua-Dong/APS-MVC.