{"type":"ScholarlyArticle","title":"Multiple Partitions Aligned Clustering","authors":[{"name":"Zhao Kang","slug":"zhao-kang","given":"Zhao","family":"Kang","affiliation":"School of Computer Science and Engineering , University of Electronic Science and Technology of China"},{"name":"Zipeng Guo","slug":"zipeng-guo","given":"Zipeng","family":"Guo","affiliation":"School of Computer Science and Engineering , University of Electronic Science and Technology of China"},{"name":"Shudong Huang","slug":"shudong-huang","given":"Shudong","family":"Huang","affiliation":"School of Computer Science and Engineering , University of Electronic Science and Technology of China"},{"name":"Siying Wang","slug":"siying-wang","given":"Siying","family":"Wang","affiliation":"School of Computer Science and Engineering , University of Electronic Science and Technology of China"},{"name":"Wenyu Chen","slug":"wenyu-chen","given":"Wenyu","family":"Chen","affiliation":"School of Computer Science and Engineering , University of Electronic Science and Technology of China"},{"name":"Yuanzhang Su","slug":"yuanzhang-su","given":"Yuanzhang","family":"Su","affiliation":"School of Foreign Languages , University of Electronic Science and Technology of China"},{"name":"Zenglin Xu","slug":"zenglin-xu","given":"Zenglin","family":"Xu","affiliation":"School of Computer Science and Engineering , University of Electronic Science and Technology of China"}],"abstract":"Multi-view clustering is an important yet challenging task due to the difficulty of integrating the information from multiple representations. Most existing multi-view clustering methods explore the heterogeneous information in the space where the data points lie. Such common practice may cause significant information loss because of unavoidable noise or inconsistency among views. Since different views admit the same cluster structure, the natural space should be all partitions. Orthogonal to existing techniques, in this paper, we propose to leverage the multi-view information by fusing partitions. Specifically, we align each partition to form a consensus cluster indicator matrix through a distinct rotation matrix. Moreover, a weight is assigned for each view to account for the clustering capacity differences of views. Finally, the basic partitions, weights, and consensus clustering are jointly learned in a unified framework. We demonstrate the effectiveness of our approach on several real datasets, where significant improvement is found over other state-of-the-art multi-view clustering methods.","doi":"10.24963/ijcai.2019/375","url":"https://ijcai.org/proceedings/2019/375","pdf":"https://ijcai.org/proceedings/2019/0375.pdf","markdown":"https://ijcai.org/proceedings/2019/375.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/375","volume":{"slug":"2019","title":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2019","url":"https://ijcai.org/proceedings/2019/","year":2019,"location":"Macao","dates":"10-16 August 2019","editors":"Sarit Kraus","isbn":"978-0-9992411-4-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2019"},"section":"Machine Learning A-L","track":"Main track","number":375,"pages":{"first":2701,"last":2707},"keywords":["Machine Learning: Unsupervised Learning","Machine Learning: Multi-instance;Multi-label;Multi-view learning","Machine Learning: Clustering"],"videos":[],"references":[{"position":1,"raw":"[Cai et al., 2013] Xiao Cai, Feiping Nie, and Heng Huang. 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