---
title: "Tensorial Multi-view Clustering with Deep Anchor Graph Projection"
authors:
  - "Wei Feng"
  - "Dongyuvan Wei"
  - "Qianqian Wang"
  - "Bo Dong"
doi: "10.24963/ijcai.2025/571"
url: "https://ijcai.org/proceedings/2025/571"
pdf: "https://ijcai.org/proceedings/2025/0571.pdf"
venue: "Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence"
year: 2025
pages: "5127-5135"
track: "Main Track"
keywords:
  - "Machine Learning: ML: Multi-view learning"
  - "Machine Learning: ML: Matrix/tensor methods"
---

# Tensorial Multi-view Clustering with Deep Anchor Graph Projection

Wei Feng, Dongyuvan Wei, Qianqian Wang, Bo Dong

*Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence*, pages 5127-5135. DOI: [10.24963/ijcai.2025/571](https://doi.org/10.24963/ijcai.2025/571)

## Abstract

Multi-view clustering (MVC) has emerged as an important unsupervised multi-view learning method that leverages consistent and complementary information to enhance clustering performance. Recently, tensorized MVC, which processes multi-view data as a tensor to capture their cross-view information, has received considerable attention. 
However, existing tensorized MVC methods generally overlook deep structures within each view and rely on post-processing to derive clustering results, leading to potential information loss and degraded performance. To address these issues, we develop Tensorial Multi-view Clustering with Deep Anchor Graph Projection (TMVC-DAGP), which performs deep projection on the anchor graph, thus improving model scalability. Besides, we utilize a sparsity regularization to eliminate the redundancy and enforce the projected anchor graph to retain a clear clustering structure. Furthermore, TMVC-DAGP leverages weighted Tensor Schatten $p$-norm to exploit the consistent and complementary information. Extensive experiments on multiple datasets demonstrate TMVC-DAGP's effectiveness and superiority.