# One-step Multi-view Clustering with Diverse Representation

arXiv: https://arxiv.org/abs/2306.05437 · HTML: https://mlsanity.com/paper/2306.05437/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 105 references that are in the map.
- First posted on arXiv 8 Jun 2023.

## Builds on (most cited first, 24 of 24)

- [A Survey of Knowledge Graph Reasoning on Graph Types: Static, Dynamic, and Multimodal](https://mlsanity.com/paper/2212.05767/) (2022)
- [Deep Graph Clustering via Dual Correlation Reduction](https://mlsanity.com/paper/2112.14772/) (2021)
- [Deep Partial Multi-View Learning](https://mlsanity.com/paper/2011.06170/) (2020)
- [Deep Fusion Clustering Network](https://mlsanity.com/paper/2012.09600/) (2020)
- [Large-scale Multi-view Subspace Clustering in Linear Time](https://mlsanity.com/paper/1911.09290/) (2019)
- [Cluster-guided Contrastive Graph Clustering Network](https://mlsanity.com/paper/2301.01098/) (2023)
- [Simple Contrastive Graph Clustering](https://mlsanity.com/paper/2205.07865/) (2022)
- [Self-Supervised Discriminative Feature Learning for Deep Multi-View Clustering](https://mlsanity.com/paper/2103.15069/) (2021)
- [Dink-Net: Neural Clustering on Large Graphs](https://mlsanity.com/paper/2305.18405/) (2023)
- [Effective and Efficient Graph Learning for Multi-view Clustering](https://mlsanity.com/paper/2108.06734/) (2021)
- [Deep Temporal Graph Clustering](https://mlsanity.com/paper/2305.10738/) (2023)
- [A Survey of Deep Graph Clustering: Taxonomy, Challenge, Application, and Open Resource](https://mlsanity.com/paper/2211.12875/) (2022)
- [Self-Supervised Temporal Graph learning with Temporal and Structural Intensity Alignment](https://mlsanity.com/paper/2302.07491/) (2023)
- [Multi-View Spectral Clustering via Structured Low-Rank Matrix Factorization](https://mlsanity.com/paper/1709.01212/) (2017)
- [Interpolation-based Contrastive Learning for Few-Label Semi-Supervised Learning](https://mlsanity.com/paper/2202.11915/) (2022)
- [Auto-weighted Multi-view Clustering for Large-scale Data](https://mlsanity.com/paper/2303.01983/) (2023)
- [Fast Continual Multi-View Clustering with Incomplete Views](https://mlsanity.com/paper/2306.02389/) (2023)
- [Local Sample-weighted Multiple Kernel Clustering with Consensus Discriminative Graph](https://mlsanity.com/paper/2207.02846/) (2022)
- [Multi-View Spectral Clustering with High-Order Optimal Neighborhood Laplacian Matrix](https://mlsanity.com/paper/2008.13539/) (2020)
- [Consistent Multiple Graph Embedding for Multi-View Clustering](https://mlsanity.com/paper/2105.04880/) (2021)
- [Multiple Kernel Clustering with Dual Noise Minimization](https://mlsanity.com/paper/2207.06041/) (2022)
- [RARE: Robust Masked Graph Autoencoder](https://mlsanity.com/paper/2304.01507/) (2023)
- [Multi-view MERA Subspace Clustering](https://mlsanity.com/paper/2305.09095/) (2023)
- [Multi-View Non-negative Matrix Factorization Discriminant Learning via Cross Entropy Loss](https://mlsanity.com/paper/2201.04726/) (2022)

## Built on by (most cited first, 10 of 10)

- [DealMVC: Dual Contrastive Calibration for Multi-view Clustering](https://mlsanity.com/paper/2308.09000/) (2023)
- [Self-supervised Learning of Hybrid Part-aware 3D Representations of 2D Gaussians and Superquadrics](https://mlsanity.com/paper/2408.10789/) (2024)
- [Scalable Incomplete Multi-View Clustering with Structure Alignment](https://mlsanity.com/paper/2308.16541/) (2023)
- [Efficient Multi-View Graph Clustering with Local and Global Structure Preservation](https://mlsanity.com/paper/2309.00024/) (2023)
- [Contrastive Continual Multi-view Clustering with Filtered Structural Fusion](https://mlsanity.com/paper/2309.15135/) (2023)
- [Scalable Attribute-Missing Graph Clustering via Neighborhood Differentiation](https://mlsanity.com/paper/2507.13368/) (2025)
- [Generalized Deep Multi-view Clustering via Causal Learning with Partially Aligned Cross-view Correspondence](https://mlsanity.com/paper/2509.16022/) (2025)
- [DWCL: Dual-Weighted Contrastive Learning for Multi-View Clustering](https://mlsanity.com/paper/2411.17354/) (2024)
- [Multi-view Clustering via Unified Multi-kernel Learning and Matrix Factorization](https://mlsanity.com/paper/2412.09065/) (2024)
- [Intra-view and Inter-view Correlation Guided Multi-view Novel Class Discovery](https://mlsanity.com/paper/2507.12029/) (2025)

## Abstract (arXiv)

Multi-view clustering has attracted broad attention due to its capacity to utilize consistent and complementary information among views. Although tremendous progress has been made recently, most existing methods undergo high complexity, preventing them from being applied to large-scale tasks. Multi-view clustering via matrix factorization is a representative to address this issue. However, most of them map the data matrices into a fixed dimension, limiting the model's expressiveness. Moreover, a range of methods suffers from a two-step process, i.e., multimodal learning and the subsequent k-means, inevitably causing a sub-optimal clustering result. In light of this, we propose a one-step multi-view clustering with diverse representation method, which incorporates multi-view learning and k-means into a unified framework. Specifically, we first project original data matrices into various latent spaces to attain comprehensive information and auto-weight them in a self-supervised manner. Then we directly use the information matrices under diverse dimensions to obtain consensus discrete clustering labels. The unified work of representation learning and clustering boosts the quality of the final results. Furthermore, we develop an efficient optimization algorithm with proven convergence to solve the resultant problem. Comprehensive experiments on various datasets demonstrate the promising clustering performance of our proposed method.