---
title: "Cooperative Multi-View Graph Learning via High-Rank Tensor Specificity"
authors:
  - "Ling Ma"
  - "Qiyu Zhong"
  - "Songxuan Shi"
  - "Hao Wei"
  - "Shunjie Yang"
  - "Junbo Lian"
  - "Qiuru Hai"
  - "Lianjin Yu"
  - "Xiangning Zeng"
  - "Yi Shan"
  - "Zhen Yang"
  - "Gengyu Lyu"
doi: "10.24963/ijcai.2026/516"
url: "https://ijcai.org/proceedings/2026/516"
pdf: "https://ijcai.org/proceedings/2026/0516.pdf"
venue: "Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence"
year: 2026
pages: "4634-4642"
track: "Main Track"
keywords:
  - "Machine Learning: Multi-view learning"
  - "Machine Learning: Clustering"
  - "Machine Learning: Unsupervised learning"
---

# Cooperative Multi-View Graph Learning via High-Rank Tensor Specificity

Ling Ma, Qiyu Zhong, Songxuan Shi, Hao Wei, Shunjie Yang, Junbo Lian, Qiuru Hai, Lianjin Yu, Xiangning Zeng, Yi Shan, Zhen Yang, Gengyu Lyu

*Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence*, pages 4634-4642. DOI: [10.24963/ijcai.2026/516](https://doi.org/10.24963/ijcai.2026/516)

## 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.