# Unsupervised Multi-view Clustering by Squeezing Hybrid Knowledge from Cross View and Each View

arXiv: https://arxiv.org/abs/2008.09990 · HTML: https://mlsanity.com/paper/2008.09990/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Aug 2020.

## Built on by (most cited first, 8 of 8)

- [A Comprehensive Survey on Spectral Clustering with Graph Structure Learning](https://mlsanity.com/paper/2501.13597/) (2025)
- [Multi-view Clustering via Deep Matrix Factorization and Partition Alignment](https://mlsanity.com/paper/2105.00277/) (2021)
- [Smoothed Multi-View Subspace Clustering](https://mlsanity.com/paper/2106.09875/) (2021)
- [Dual-Optimized Adaptive Graph Reconstruction for Multi-View Graph Clustering](https://mlsanity.com/paper/2410.22983/) (2024)
- [Advanced Unsupervised Learning: A Comprehensive Overview of Multi-View Clustering Techniques](https://mlsanity.com/paper/2512.05169/) (2025)
- [Multi-view Clustering with Deep Matrix Factorization and Global Graph Refinement](https://mlsanity.com/paper/2105.00248/) (2021)
- [Uncertainty Quantification via H"older Divergence for Multi-View Representation Learning](https://mlsanity.com/paper/2411.00826/) (2024)
- [Deep Matrix Factorization with Adaptive Weights for Multi-View Clustering](https://mlsanity.com/paper/2412.02292/) (2024)

## Abstract (arXiv)

Multi-view clustering methods have been a focus in recent years because of their superiority in clustering performance. However, typical traditional multi-view clustering algorithms still have shortcomings in some aspects, such as removal of redundant information, utilization of various views and fusion of multi-view features. In view of these problems, this paper proposes a new multi-view clustering method, low-rank subspace multi-view clustering based on adaptive graph regularization. We construct two new data matrix decomposition models into a unified optimization model. In this framework, we address the significance of the common knowledge shared by the cross view and the unique knowledge of each view by presenting new low-rank and sparse constraints on the sparse subspace matrix. To ensure that we achieve effective sparse representation and clustering performance on the original data matrix, adaptive graph regularization and unsupervised clustering constraints are also incorporated in the proposed model to preserve the internal structural features of the data. Finally, the proposed method is compared with several state-of-the-art algorithms. Experimental results for five widely used multi-view benchmarks show that our proposed algorithm surpasses other state-of-the-art methods by a clear margin.