{"type":"ScholarlyArticle","title":"Cross-View Diversity Embedded Consensus Learning for Multi-View Clustering","authors":[{"name":"Chong Peng","slug":"chong-peng","given":"Chong","family":"Peng","affiliation":"College of Computer Science and Technology , Qingdao University , China"},{"name":"Kai Zhang","slug":"kai-zhang","given":"Kai","family":"Zhang","affiliation":"College of Computer Science and Technology , Qingdao University , China"},{"name":"Yongyong Chen","slug":"yongyong-chen","given":"Yongyong","family":"Chen","affiliation":"School of Computer Science and Technology , Harbin Institute of Technology (Shenzhen) , China"},{"name":"Chenglizhao Chen","slug":"chenglizhao-chen","given":"Chenglizhao","family":"Chen","affiliation":"College of Computer Science and Technology , China University of Petroleum (East China) , China"},{"name":"Qiang Cheng","slug":"qiang-cheng","given":"Qiang","family":"Cheng","affiliation":"Department of Computer Science , University of Kentucky , USA"}],"abstract":"Multi-view clustering (MVC) has garnered significant attention in recent studies. In this paper, we propose a novel MVC method, named CCL-MVC. The novel method constructs a cross-order neighbor tensor of multi-view data to recover a low-rank essential tensor, preserves noise-free, comprehensive, and complementary cross-order relationships among the samples. Furthermore, it constructs a consensus representation matrix by fusing the low-rank essential tensor with auto-adjusted cross-view diversity embedding, fully exploiting both consensus and discriminative information of the data. An effective optimization algorithm is developed, which is theoretically guaranteed to converge. Extensive experimental results confirm the effectiveness of the proposed method.","doi":"10.24963/ijcai.2024/529","url":"https://ijcai.org/proceedings/2024/529","pdf":"https://ijcai.org/proceedings/2024/0529.pdf","markdown":"https://ijcai.org/proceedings/2024/529.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/529","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":529,"pages":{"first":4788,"last":4796},"keywords":["Machine Learning: ML: Multi-view learning","Machine Learning: ML: Clustering"],"videos":[],"references":[{"position":1,"raw":"[Brbić and Kopriva, 2018] Maria Brbić and Ivica Kopriva. 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