{"type":"ScholarlyArticle","title":"Efficient Multi-view Clustering via Reinforcement Contrastive Learning","authors":[{"name":"Qianqian Wang","slug":"qianqian-wang","given":"Qianqian","family":"Wang","affiliation":""},{"name":"Haiming Xu","slug":"haiming-xu","given":"Haiming","family":"Xu","affiliation":""},{"name":"Zihao Zhang","slug":"zihao-zhang","given":"Zihao","family":"Zhang","affiliation":""},{"name":"Zhiqiang Tao","slug":"zhiqiang-tao","given":"Zhiqiang","family":"Tao","affiliation":""},{"name":"Quanxue Gao","slug":"quanxue-gao","given":"Quanxue","family":"Gao","affiliation":""}],"abstract":"Contrastive multi-view clustering has demonstrated remarkable potential in complex data analysis, yet existing approaches face two critical challenges: difficulty in constructing high-quality positive and negative pairs and high computational overhead due to static optimization strategies. To address these challenges, we propose an innovative efficient Multi-View Clustering framework with Reinforcement Contrastive Learning (EMVCRCL). Our key innovation is developing a reinforcement contrastive learning paradigm for dynamic clustering optimization. First, we leverage multi-view contrastive learning to obtain latent features, which are then sent to the reinforcement learning module to refine low-quality features. Specifically, it selects high-confident features to guide the positive/negative pair construction of contrastive learning. For the low-confident features, it utilizes the prior balanced distribution to adjust their assignment. Extensive experimental results showcase the effectiveness and superiority of our proposed method on multiple benchmark datasets.","doi":"10.24963/ijcai.2025/708","url":"https://ijcai.org/proceedings/2025/708","pdf":"https://ijcai.org/proceedings/2025/0708.pdf","markdown":"https://ijcai.org/proceedings/2025/708.md","bibtex":"https://ijcai.org/proceedings/2025/bibtex/708","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":708,"pages":{"first":6361,"last":6369},"keywords":["Machine Learning: ML: Reinforcement learning"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}