{"type":"ScholarlyArticle","title":"Cooperative Multi-View Graph Learning via High-Rank Tensor Specificity","authors":[{"name":"Ling Ma","slug":"ling-ma","given":"Ling","family":"Ma","affiliation":""},{"name":"Qiyu Zhong","slug":"qiyu-zhong","given":"Qiyu","family":"Zhong","affiliation":""},{"name":"Songxuan Shi","slug":"songxuan-shi","given":"Songxuan","family":"Shi","affiliation":""},{"name":"Hao Wei","slug":"hao-wei","given":"Hao","family":"Wei","affiliation":""},{"name":"Shunjie Yang","slug":"shunjie-yang","given":"Shunjie","family":"Yang","affiliation":""},{"name":"Junbo Lian","slug":"junbo-lian","given":"Junbo","family":"Lian","affiliation":""},{"name":"Qiuru Hai","slug":"qiuru-hai","given":"Qiuru","family":"Hai","affiliation":""},{"name":"Lianjin Yu","slug":"lianjin-yu","given":"Lianjin","family":"Yu","affiliation":""},{"name":"Xiangning Zeng","slug":"xiangning-zeng","given":"Xiangning","family":"Zeng","affiliation":""},{"name":"Yi Shan","slug":"yi-shan","given":"Yi","family":"Shan","affiliation":""},{"name":"Zhen Yang","slug":"zhen-yang","given":"Zhen","family":"Yang","affiliation":""},{"name":"Gengyu Lyu","slug":"gengyu-lyu","given":"Gengyu","family":"Lyu","affiliation":""}],"abstract":"Graph-based multi-view clustering, with its ability to mine potential associations between samples, has attracted extensive attention. To capture high-order correlations, tensor-based frameworks have been introduced to model multiple graphs jointly.  Although these methods have achieved promising performance, existing methods mainly focus on stacking consistency graphs with low-rank constraints, while overlooking  high-order specificity within diversity graphs, and the exploration of fine-grained diversity information among different samples across views remains insufficient. To address these issues, we propose High-Rank Tensor Specificity Induced Cooperative Multi-ViewGraph Learning (HTS-CMGL). Specifically, we first obtain the consistency graphs and diversity graphs from multi-view data, which are further reconstructed into the consistency tensor and the diversity tensor, respectively. Subsequently, a novel Enhanced Tensor Rank (ETR) is imposed on the consistency tensor, which is a tighter approximation of the tensor rank and is more noisy-robust to explore the high-order consistency. Meanwhile, we design a new Tensor High-Rank Logarithmic Norm (THLN) on the diversity tensor. By leveraging a unique high-rank constraint mechanism,  THLN not only actively preserves high-rank and informative features, but also simultaneously captures view-level and sample-level diversity information. Extensive experiments on multiple datasets demonstrate the effectiveness of our proposed method on clustering multi-view data.","doi":"10.24963/ijcai.2026/516","url":"https://ijcai.org/proceedings/2026/516","pdf":"https://ijcai.org/proceedings/2026/0516.pdf","markdown":"https://ijcai.org/proceedings/2026/516.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/516","volume":{"slug":"2026","title":"Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2026","url":"https://ijcai.org/proceedings/2026/","year":2026,"location":"Bremen, Germany","dates":"15-21 August 2026","editors":"Diego Calvanese","isbn":"978-1-956792-09-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2026"},"section":"Machine Learning","track":"Main Track","number":516,"pages":{"first":4634,"last":4642},"keywords":["Machine Learning: Multi-view learning","Machine Learning: Clustering","Machine Learning: Unsupervised learning"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}