{"type":"ScholarlyArticle","title":"Robust Graph Contrastive Learning for Incomplete Multi-view Clustering","authors":[{"name":"Deyin Zhuang","slug":"deyin-zhuang","given":"Deyin","family":"Zhuang","affiliation":""},{"name":"Jian Dai","slug":"jian-dai","given":"Jian","family":"Dai","affiliation":""},{"name":"Xingfeng Li","slug":"xingfeng-li","given":"Xingfeng","family":"Li","affiliation":""},{"name":"Xi Wu","slug":"xi-wu","given":"Xi","family":"Wu","affiliation":""},{"name":"Yuan Sun","slug":"yuan-sun","given":"Yuan","family":"Sun","affiliation":""},{"name":"Zhenwen Ren","slug":"zhenwen-ren","given":"Zhenwen","family":"Ren","affiliation":""}],"abstract":"In recent years, multi-view clustering (MVC) has become a promising approach for analyzing heterogeneous multi-source data. However, during the collection of multi-view data, factors such as environmental interference or sensor failure often lead to the loss of view sample data, resulting in incomplete multi-view clustering (IMVC). Graph contrastive IMVC has demonstrated promising performance as an effective solution, which typically utilizes in-graph instances as positive pairs and out-of-graph instances as negative pairs. However, the construction of positive and negative pairs in this paradigm inevitably leads to graph noise Correspondence (GNC). To this end, we propose a new IMVC framework, namely robust graph contrastive learning (RGCL). Specifically, RGCL first completes the missing data by using a multi-view consistency transfer relationship graph. Then, to mitigate the impact of false negative pairs from graph contrastive, we propose noise-robust graph contrastive learning to mine intra-view consistency accurately. Finally, we present cross-view graph-level alignment to fully exploit the complementary information across different views. Experimental results on the six multi-view datasets demonstrate that our RGCL exhibits superiority and effectiveness compared with 9 state-of-the-art IMVC methods. The source code is available at https://github.com/DYZ163/RGCL.git.","doi":"10.24963/ijcai.2025/810","url":"https://ijcai.org/proceedings/2025/810","pdf":"https://ijcai.org/proceedings/2025/0810.pdf","markdown":"https://ijcai.org/proceedings/2025/810.md","bibtex":"https://ijcai.org/proceedings/2025/bibtex/810","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":810,"pages":{"first":7282,"last":7290},"keywords":["Machine Learning: ML: Multi-view learning","Machine Learning: ML: Clustering","Machine Learning: ML: Multi-modal learning","Machine Learning: ML: Unsupervised learning"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}