{"type":"ScholarlyArticle","title":"Content-style Disentanglement Guided Representation Learning for Deep Incomplete Multi-view Clustering","authors":[{"name":"Enze Ji","slug":"enze-ji","given":"Enze","family":"Ji","affiliation":""},{"name":"Meng Liu","slug":"meng-liu","given":"Meng","family":"Liu","affiliation":""},{"name":"Yuzhe Li","slug":"yuzhe-li","given":"Yuzhe","family":"Li","affiliation":""},{"name":"Zhikui Chen","slug":"zhikui-chen","given":"Zhikui","family":"Chen","affiliation":""}],"abstract":"Deep incomplete multi-view clustering methods can mine patterns of incomplete multi-view data without labels, gaining great attention in various domains. However, current methods are obsessed with aligning view-specific representations from available samples with complete views to learn view-invariant information for missing data recovery, ignoring fruitful view-complementary information beneficial to data imputations. Meanwhile, such the view-invariant learning often leads to the problem of dominant view dependency that models tend to over-rely on views with clear clustering structures and neglect weaker ones, causing a performance bottleneck. Therefore, a content-style disentanglement guided representation learning is proposed for the incomplete multi-view clustering (CSMVC). Specifically, CSMVC designs a view-specific dual-representation learning architecture that extracts view-invariant content representations and view-specific style representations from self-supervised reconstructions of each view with the guidance of the Hilbert schmidt independence criterion. Then, it performs a content-centered style-modulated representation imputation mechanism to infer missing data via fully utilizing inter-view complementary and consistent information. Meanwhile, it devises a cross-view consensus structure mining strategy to capture the clustering structure with intra-cluster compactness and inter-cluster separability from attention-based fusion representations that adaptively aggregate content representations and style representations of each view. Finally, comprehensive experiments show that CSMVC outperforms state-of-the-art methods.","doi":"10.24963/ijcai.2026/489","url":"https://ijcai.org/proceedings/2026/489","pdf":"https://ijcai.org/proceedings/2026/0489.pdf","markdown":"https://ijcai.org/proceedings/2026/489.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/489","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":489,"pages":{"first":4394,"last":4402},"keywords":["Machine Learning: Clustering","Machine Learning: Multi-view learning","Machine Learning: Deep learning architectures"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}