{"type":"ScholarlyArticle","title":"Dual-Topology Learning with Adaptive Anchors for Multi-View Clustering","authors":[{"name":"Chenglong Zhang","slug":"chenglong-zhang","given":"Chenglong","family":"Zhang","affiliation":""},{"name":"Chao Zhang","slug":"chao-zhang","given":"Chao","family":"Zhang","affiliation":""},{"name":"Junhao Zhang","slug":"junhao-zhang","given":"Junhao","family":"Zhang","affiliation":""},{"name":"Junyi Guan","slug":"junyi-guan","given":"Junyi","family":"Guan","affiliation":""},{"name":"Xianzhong Zhou","slug":"xianzhong-zhou","given":"Xianzhong","family":"Zhou","affiliation":""},{"name":"Bo Wang","slug":"bo-wang","given":"Bo","family":"Wang","affiliation":""},{"name":"Huaxiong Li","slug":"huaxiong-li","given":"Huaxiong","family":"Li","affiliation":""}],"abstract":"As a prominent paradigm for large-scale unsupervised learning, anchor-based multi-view clustering aims to reveal the latent structures across heterogeneous data representations with high efficiency. Despite achieving some progress, existing methods typically suffer from the following two limitations. Firstly, they rely on pre-constructed anchors or rigid constraints (e.g., orthogonality), whereas the intrinsic topological correlations among anchors are completely discarded. Secondly, most existing methods either perform clustering directly on the bipartite graph or treat different views equally, thereby failing to capture the global structural information. To this end, the Dual-tOpology learning with adapTive Anchors (DOTA) is proposed, which not only learns the sample-anchor relationship (i.e., bipartite graph) but also preserves the topology structure among anchors, significantly enhancing the discriminability of learned representation while preserving the underlying data manifold. By integrating an adaptive view-weighting strategy to balance view contributions, DOTA derives discriminative global sample embeddings by propagating spectral information through the bipartite graph.  Extensive experiments on benchmark datasets demonstrate the superiority of DOTA.","doi":"10.24963/ijcai.2026/590","url":"https://ijcai.org/proceedings/2026/590","pdf":"https://ijcai.org/proceedings/2026/0590.pdf","markdown":"https://ijcai.org/proceedings/2026/590.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/590","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":590,"pages":{"first":5297,"last":5305},"keywords":["Machine Learning: Multi-view learning","Machine Learning: Clustering","Machine Learning: Unsupervised learning"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}