One-Step Self-Aligned Anchor Learning for Multi-View Clustering
TL;DR
A novel mixed anchor strategy is introduced, which effectively bypasses the necessity of manual baseline selection while simultaneously capturing both view-specific and cross-view information and can stabilize clustering performance at a consistently high level.
Abstract
Multi-view anchor graph clustering has emerged as a high-efficiency paradigm of multi-view learning. However, how to design an effective anchor alignment mechanism within this framework remains an open challenge, which is formally termed the Anchor-Unaligned Problem (AUP). Current research fails to adequately address two pivotal aspects of this challenge: first, the construction of anchor graphs and the alignment of anchors are two independent stages, overlooking their potential synergistic reinforcement; second, selecting anchors from different views as an alignment baseline often renders the clustering performance highly sensitive to the baseline choice. To address these issues, we propose a unified framework termed One-Step Self-Aligned Anchor Learning for Multi-View Clustering (OSAA-MVC). Departing from conventional two-stage strategies, we integrate anchor alignment and anchor graph construction into a joint optimization process, thereby enabling their mutual reinforcement to improve clustering performance. To avoid the baseline selection issue, we introduce a novel mixed anchor strategy, which effectively bypasses the necessity of manual baseline selection while simultaneously capturing both view-specific and cross-view information. This strategy can stabilize clustering performance at a consistently high level. Extensive experiments demonstrate the superior efficiency and effectiveness of our proposed method compared to state-of-the-art competitors. The code is available at https://github.com/Jiamiao2024/OSAA-MVC.