{"type":"ScholarlyArticle","title":"Federated Multi-View Clustering via Tensor Factorization","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 an effective method to process massive unlabeled multi-view data. Since data of different views may be collected and held by different parties, it becomes impractical to train a multi-view clustering model in a centralized way, for the sake of privacy. However, federated multi-view clustering is challenging because multi-view learning has to consider the complementary and consistent information between each view distributed across different clients. For another, efficiency is highly expected in federated scenarios. Therefore, we propose a novel federated multi-view clustering method with tensor factorization (TensorFMVC), which is built based on K-means and hence is more efficient. Besides, TensorFMVC avoids initializing centroids to address the performance degradation of K-means due to its sensitivity to centroid initialization. A three-order tensor stacked by cluster assignment matrices is introduced to exploit the complementary information and spatial structure of different views. Furthermore, we divide the optimization into several subproblems and develop a federated optimization approach to support cooperative model training. Extensive experiments on several datasets demonstrate that our proposed method exhibits superior performance in federated multi-view clustering.","doi":"10.24963/ijcai.2024/438","url":"https://ijcai.org/proceedings/2024/438","pdf":"https://ijcai.org/proceedings/2024/0438.pdf","markdown":"https://ijcai.org/proceedings/2024/438.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/438","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":438,"pages":{"first":3962,"last":3970},"keywords":["Machine Learning: ML: Federated learning","Machine Learning: ML: Multi-view learning"],"videos":[],"references":[{"position":1,"raw":"[Brbić and Kopriva, 2018] Maria Brbić and Ivica Kopriva. 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