---
title: "One-Step Self-Aligned Anchor Learning for Multi-View Clustering"
authors:
  - "Zijian Chen"
  - "Miao Jia"
  - "Xingchen Hu"
  - "Jiyuan Liu"
  - "Huan Chen"
  - "Siwei Wang"
  - "Jincai Huang"
doi: "10.24963/ijcai.2026/468"
url: "https://ijcai.org/proceedings/2026/468"
pdf: "https://ijcai.org/proceedings/2026/0468.pdf"
venue: "Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence"
year: 2026
pages: "4206-4214"
track: "Main Track"
keywords:
  - "Machine Learning: Clustering"
  - "Machine Learning: Unsupervised learning"
  - "Machine Learning: Multi-view learning"
---

# One-Step Self-Aligned Anchor Learning for Multi-View Clustering

Zijian Chen, Miao Jia, Xingchen Hu, Jiyuan Liu, Huan Chen, Siwei Wang, Jincai Huang

*Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence*, pages 4206-4214. DOI: [10.24963/ijcai.2026/468](https://doi.org/10.24963/ijcai.2026/468)

## 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.