{"type":"ScholarlyArticle","title":"One-Step Self-Aligned Anchor Learning for Multi-View Clustering","authors":[{"name":"Zijian Chen","slug":"zijian-chen","given":"Zijian","family":"Chen","affiliation":""},{"name":"Miao Jia","slug":"miao-jia","given":"Miao","family":"Jia","affiliation":""},{"name":"Xingchen Hu","slug":"xingchen-hu","given":"Xingchen","family":"Hu","affiliation":""},{"name":"Jiyuan Liu","slug":"jiyuan-liu","given":"Jiyuan","family":"Liu","affiliation":""},{"name":"Huan Chen","slug":"huan-chen","given":"Huan","family":"Chen","affiliation":""},{"name":"Siwei Wang","slug":"siwei-wang","given":"Siwei","family":"Wang","affiliation":""},{"name":"Jincai Huang","slug":"jincai-huang","given":"Jincai","family":"Huang","affiliation":""}],"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.","doi":"10.24963/ijcai.2026/468","url":"https://ijcai.org/proceedings/2026/468","pdf":"https://ijcai.org/proceedings/2026/0468.pdf","markdown":"https://ijcai.org/proceedings/2026/468.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/468","volume":{"slug":"2026","title":"Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2026","url":"https://ijcai.org/proceedings/2026/","year":2026,"location":"Bremen, Germany","dates":"15-21 August 2026","editors":"Diego Calvanese","isbn":"978-1-956792-09-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2026"},"section":"Machine Learning","track":"Main Track","number":468,"pages":{"first":4206,"last":4214},"keywords":["Machine Learning: Clustering","Machine Learning: Unsupervised learning","Machine Learning: Multi-view learning"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}