{"type":"ScholarlyArticle","title":"MASTER: A Multi-granularity Invariant Structure Clustering Scheme for Multi-view Clustering","authors":[{"name":"Suixue Wang","slug":"suixue-wang","given":"Suixue","family":"Wang","affiliation":""},{"name":"Shilin Zhang","slug":"shilin-zhang","given":"Shilin","family":"Zhang","affiliation":""},{"name":"Qingchen Zhang","slug":"qingchen-zhang","given":"Qingchen","family":"Zhang","affiliation":""},{"name":"Peng Li","slug":"peng-li","given":"Peng","family":"Li","affiliation":""},{"name":"Weiliang Huo","slug":"weiliang-huo","given":"Weiliang","family":"Huo","affiliation":""}],"abstract":"Deep multi-view clustering has attracted increasing attention in the pattern mining of data. However, most of them perform self-learning mechanisms in a single space, ignoring the fruitful structural information hidden in different-level feature spaces. Meanwhile, they conduct the reconstruction constraint to learn generalized representations of samples, failing to explore the discriminative ability of complementary and consistent information. To address the challenges, a multi-granularity invariant structure clustering scheme (MASTER) is proposed to define a bottom-up process that extracts multi-level information in sample, neighborhood, and category granularities from low-level, high-level, and semantics feature space, respectively. Specifically, it leverages the self-learning reconstruction with information-theoretic overclustering to capture invariant sample structure in the low-level feature space. Then, it models data diffusion of the clustering process in the reliable neighborhood to capture invariant local structure in the high-level feature space. Meanwhile, it defines dual divergences induced by the space geometry to capture invariant global structure in the semantics space. Finally, extensive experiments on 8 real-world datasets show that MASTER achieves state-of-the-art performance compared to 11 baselines.","doi":"10.24963/ijcai.2025/714","url":"https://ijcai.org/proceedings/2025/714","pdf":"https://ijcai.org/proceedings/2025/0714.pdf","markdown":"https://ijcai.org/proceedings/2025/714.md","bibtex":"https://ijcai.org/proceedings/2025/bibtex/714","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":714,"pages":{"first":6415,"last":6423},"keywords":["Machine Learning: ML: Clustering","Machine Learning: ML: Multi-view learning"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}