{"type":"ScholarlyArticle","title":"Efficient Federated Multi-View Clustering with Integrated Matrix Factorization and K-Means","authors":[{"name":"Wei Feng","slug":"wei-feng","given":"Wei","family":"Feng","affiliation":"School of Computer Science and Technology , Xi'an Jiaotong University , Xi'an , China"},{"name":"Zhenwei Wu","slug":"zhenwei-wu","given":"Zhenwei","family":"Wu","affiliation":"School of Computer Science and Technology , Xi'an Jiaotong University , Xi'an , China"},{"name":"Qianqian Wang","slug":"qianqian-wang","given":"Qianqian","family":"Wang","affiliation":"School of Telecommunications Engineering , Xidian University , Xi'an , China"},{"name":"Bo Dong","slug":"bo-dong","given":"Bo","family":"Dong","affiliation":"School of Continuing Education , Xi'an Jiaotong University , Xi'an , China"},{"name":"Zhiqiang Tao","slug":"zhiqiang-tao","given":"Zhiqiang","family":"Tao","affiliation":"School of Information , Rochester Institute of Technology , NY , USA;"},{"name":"Quanxue Gao","slug":"quanxue-gao","given":"Quanxue","family":"Gao","affiliation":"School of Telecommunications Engineering , Xidian University , Xi'an , China"}],"abstract":"Multi-view clustering is a popular unsupervised multi-view learning method. Real-world multi-view data are often distributed across multiple entities, presenting a challenge for performing multi-view clustering. Federated learning provides a solution by enabling multiple entities to collaboratively train a global model. However, existing federated multi-view clustering methods usually conduct feature extraction and clustering in separate steps, potentially leading to a degradation in clustering performance. To address this issue and for the sake of efficiency, we propose a novel Federated Multi-View Clustering method with Integrated Matrix Factorization and K-Means (FMVC-IMK), which integrates matrix factorization and multi-view K-means into one step. Additionally, an adaptive weight is employed to balance the influence of data from each view. FMVC-IMK further incorporates a graph-based regularizer to preserve the original data's geometric structure within the learned global clustering structure. We also develop a federated optimization approach to collaboratively learn a global clustering result without sharing any original data. Experimental results on multiple datasets demonstrate the effectiveness of FMVC-IMK.","doi":"10.24963/ijcai.2024/439","url":"https://ijcai.org/proceedings/2024/439","pdf":"https://ijcai.org/proceedings/2024/0439.pdf","markdown":"https://ijcai.org/proceedings/2024/439.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/439","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Machine Learning","track":"Main Track","number":439,"pages":{"first":3971,"last":3979},"keywords":["Machine Learning: ML: Multi-view learning","Machine Learning: ML: Federated learning","Data Mining: DM: Privacy-preserving data mining"],"videos":[],"references":[{"position":1,"raw":"[Al-Stouhi and Reddy, 2014] Samir Al-Stouhi and Chan- dan K Reddy. 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