{"type":"ScholarlyArticle","title":"Consensus-Guided Incomplete Multi-view Clustering via Cross-view Affinities Learning","authors":[{"name":"Qian Liu","slug":"qian-liu","given":"Qian","family":"Liu","affiliation":""},{"name":"Huibing Wang","slug":"huibing-wang","given":"Huibing","family":"Wang","affiliation":""},{"name":"Jinjia Peng","slug":"jinjia-peng","given":"Jinjia","family":"Peng","affiliation":""},{"name":"Yawei Chen","slug":"yawei-chen","given":"Yawei","family":"Chen","affiliation":""},{"name":"Mingze Yao","slug":"mingze-yao","given":"Mingze","family":"Yao","affiliation":""},{"name":"Xianping Fu","slug":"xianping-fu","given":"Xianping","family":"Fu","affiliation":""},{"name":"Yang Wang","slug":"yang-wang","given":"Yang","family":"Wang","affiliation":""}],"abstract":"Incomplete multi-view clustering (IMC) has garnered substantial attention due to its capacity to handle unlabeled data. Existing methods predominantly explore pairwise consistency between every two views. However, such consistency is highly susceptible to missing samples and outliers within a certain view and thus deviates from the true clustering distribution. Moreover, dual-view interaction neglects the collaboration effects of multiple views, making it challenging to capture the holistic characteristics across views. In response to these issues, we propose a novel Consensus-Guided Incomplete Multi-view Clustering via Cross-view Affinities Learning (CAL). Specifically, CAL reconstructs views with available instances to mine sample-wise affinities and harness comprehensive content information within views. Subsequently, to extract clean structural information, CAL imposes a structured sparse constraint on the representation tensor to eliminate biased errors. Furthermore, by integrating the consensus representation into a representation tensor, CAL can employ high-order interaction of multiple views to depict the semantic correlation between views while acquiring a unified structural graph across multiple views. Extensive experiments on seven benchmark datasets demonstrate that CAL outperforms some state-of-the-art methods in clustering performance. The code is available at https://github.com/whbdmu/CAL.","doi":"10.24963/ijcai.2025/641","url":"https://ijcai.org/proceedings/2025/641","pdf":"https://ijcai.org/proceedings/2025/0641.pdf","markdown":"https://ijcai.org/proceedings/2025/641.md","bibtex":"https://ijcai.org/proceedings/2025/bibtex/641","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":641,"pages":{"first":5761,"last":5769},"keywords":["Machine Learning: ML: Matrix/tensor methods","Machine Learning: ML: Clustering","Machine Learning: ML: Multi-view learning"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}