{"type":"ScholarlyArticle","title":"Multi-view Regression Clustering via Low-Rank Manifold Decomposition","authors":[{"name":"Xiaowei Zhao","slug":"xiaowei-zhao","given":"Xiaowei","family":"Zhao","affiliation":""},{"name":"Xinyu Kou","slug":"xinyu-kou","given":"Xinyu","family":"Kou","affiliation":""},{"name":"Yan Chen","slug":"yan-chen","given":"Yan","family":"Chen","affiliation":""},{"name":"Linrui Xie","slug":"linrui-xie","given":"Linrui","family":"Xie","affiliation":""},{"name":"Qiang Zhang","slug":"qiang-zhang","given":"Qiang","family":"Zhang","affiliation":""},{"name":"Liang Du","slug":"liang-du","given":"Liang","family":"Du","affiliation":""}],"abstract":"Similarity-graph-based multi-view clustering is effective in capturing non-Gaussian cluster structures, yet it suffers from an inherent conflict between scalability and geometry-consistent cluster assignment. In particular, cluster labels are typically inferred via secondary clustering rather than being learned directly from the underlying data geometry. Although anchor-based extensions alleviate the computational burden, they introduce the  challenge of anchor-number selection.\r\nTo address these limitations, we propose Multi-view Clustering via Manifold Decomposition (MvMD), which directly infers cluster labels from multi-view data without explicit similarity graph construction or anchor selection. MvMD reformulates multi-view clustering as a unified multi-view regression problem, where cluster labels are optimized as model variables. To improve robustness against missing small clusters, a global balance regularization is incorporated. Meanwhile, local geometric consistency and the low-rank structure of cluster assignments are jointly enforced through a symmetric matrix factorization scheme, which is efficiently realized via a truncated SVD-based low-rank approximation.\r\nExtensive experiments on benchmark datasets validate the effectiveness and efficiency of the proposed MvMD. The code is available at https://github.com/Vince-Doit/MvMD.","doi":"10.24963/ijcai.2026/599","url":"https://ijcai.org/proceedings/2026/599","pdf":"https://ijcai.org/proceedings/2026/0599.pdf","markdown":"https://ijcai.org/proceedings/2026/599.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/599","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":599,"pages":{"first":5378,"last":5386},"keywords":["Machine Learning: Multi-view learning","Machine Learning: Unsupervised learning","Machine Learning: Clustering"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}