{"type":"ScholarlyArticle","title":"Tensorial Multi-view Clustering with Deep Anchor Graph Projection","authors":[{"name":"Wei Feng","slug":"wei-feng","given":"Wei","family":"Feng","affiliation":""},{"name":"Dongyuvan Wei","slug":"dongyuvan-wei","given":"Dongyuvan","family":"Wei","affiliation":""},{"name":"Qianqian Wang","slug":"qianqian-wang","given":"Qianqian","family":"Wang","affiliation":""},{"name":"Bo Dong","slug":"bo-dong","given":"Bo","family":"Dong","affiliation":""}],"abstract":"Multi-view clustering (MVC) has emerged as an important unsupervised multi-view learning method that leverages consistent and complementary information to enhance clustering performance. Recently, tensorized MVC, which processes multi-view data as a tensor to capture their cross-view information, has received considerable attention. \r\nHowever, existing tensorized MVC methods generally overlook deep structures within each view and rely on post-processing to derive clustering results, leading to potential information loss and degraded performance. To address these issues, we develop Tensorial Multi-view Clustering with Deep Anchor Graph Projection (TMVC-DAGP), which performs deep projection on the anchor graph, thus improving model scalability. Besides, we utilize a sparsity regularization to eliminate the redundancy and enforce the projected anchor graph to retain a clear clustering structure. Furthermore, TMVC-DAGP leverages weighted Tensor Schatten $p$-norm to exploit the consistent and complementary information. Extensive experiments on multiple datasets demonstrate TMVC-DAGP's effectiveness and superiority.","doi":"10.24963/ijcai.2025/571","url":"https://ijcai.org/proceedings/2025/571","pdf":"https://ijcai.org/proceedings/2025/0571.pdf","markdown":"https://ijcai.org/proceedings/2025/571.md","bibtex":"https://ijcai.org/proceedings/2025/bibtex/571","volume":{"slug":"2025","title":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2025","url":"https://ijcai.org/proceedings/2025/","year":2025,"location":"Montreal, Canada","dates":"16-22 August 2025","editors":"James Kwok","isbn":"978-1-956792-06-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2025"},"section":"Machine Learning","track":"Main Track","number":571,"pages":{"first":5127,"last":5135},"keywords":["Machine Learning: ML: Multi-view learning","Machine Learning: ML: Matrix/tensor methods"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}