---
title: "Multi-view Regression Clustering via Low-Rank Manifold Decomposition"
authors:
  - "Xiaowei Zhao"
  - "Xinyu Kou"
  - "Yan Chen"
  - "Linrui Xie"
  - "Qiang Zhang"
  - "Liang Du"
doi: "10.24963/ijcai.2026/599"
url: "https://ijcai.org/proceedings/2026/599"
pdf: "https://ijcai.org/proceedings/2026/0599.pdf"
venue: "Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence"
year: 2026
pages: "5378-5386"
track: "Main Track"
keywords:
  - "Machine Learning: Multi-view learning"
  - "Machine Learning: Unsupervised learning"
  - "Machine Learning: Clustering"
---

# Multi-view Regression Clustering via Low-Rank Manifold Decomposition

Xiaowei Zhao, Xinyu Kou, Yan Chen, Linrui Xie, Qiang Zhang, Liang Du

*Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence*, pages 5378-5386. DOI: [10.24963/ijcai.2026/599](https://doi.org/10.24963/ijcai.2026/599)

## Abstract

Similarity-graph-based multi-view clustering is effective in capturing non-Gaussian cluster structures, yet it suffers from an inherent conflict between scalability and geometry-consistent cluster assignment. In particular, cluster labels are typically inferred via secondary clustering rather than being learned directly from the underlying data geometry. Although anchor-based extensions alleviate the computational burden, they introduce the  challenge of anchor-number selection.
To address these limitations, we propose Multi-view Clustering via Manifold Decomposition (MvMD), which directly infers cluster labels from multi-view data without explicit similarity graph construction or anchor selection. MvMD reformulates multi-view clustering as a unified multi-view regression problem, where cluster labels are optimized as model variables. To improve robustness against missing small clusters, a global balance regularization is incorporated. Meanwhile, local geometric consistency and the low-rank structure of cluster assignments are jointly enforced through a symmetric matrix factorization scheme, which is efficiently realized via a truncated SVD-based low-rank approximation.
Extensive experiments on benchmark datasets validate the effectiveness and efficiency of the proposed MvMD. The code is available at https://github.com/Vince-Doit/MvMD.