# Generative Partial Multi-View Clustering

arXiv: https://arxiv.org/abs/2003.13088 · HTML: https://mlsanity.com/paper/2003.13088/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Mar 2020.

## Built on by (most cited first, 25 of 25)

- [Deep Incomplete Multi-view Clustering with Cross-view Partial Sample and Prototype Alignment](https://mlsanity.com/paper/2303.15689/) (2023)
- [Investigating and Mitigating the Side Effects of Noisy Views for Self-Supervised Clustering Algorithms in Practical Multi-View Scenarios](https://mlsanity.com/paper/2303.17245/) (2023)
- [Fast Continual Multi-View Clustering with Incomplete Views](https://mlsanity.com/paper/2306.02389/) (2023)
- [Incomplete Multi-view Clustering via Diffusion Contrastive Generation](https://mlsanity.com/paper/2503.09185/) (2025)
- [Incomplete Multi-view Clustering via Cross-view Relation Transfer](https://mlsanity.com/paper/2112.00739/) (2021)
- [Imputation-free and Alignment-free: Incomplete Multi-view Clustering Driven by Consensus Semantic Learning](https://mlsanity.com/paper/2505.11182/) (2025)
- [Deep Incomplete Multi-view Clustering with Distribution Dual-Consistency Recovery Guidance](https://mlsanity.com/paper/2503.11017/) (2025)
- [Global Graph Propagation with Hierarchical Information Transfer for Incomplete Contrastive Multi-view Clustering](https://mlsanity.com/paper/2502.19291/) (2025)
- [Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios](https://mlsanity.com/paper/2505.21387/) (2025)
- [ACTIVE:Augmentation-Free Graph Contrastive Learning for Partial Multi-View Clustering](https://mlsanity.com/paper/2203.00186/) (2022)
- [Incomplete Multi-view Clustering via Diffusion Completion](https://mlsanity.com/paper/2305.11489/) (2023)
- [URRL-IMVC: Unified and Robust Representation Learning for Incomplete Multi-View Clustering](https://mlsanity.com/paper/2407.09120/) (2024)
- [Self-supervised Multi-view Clustering in Computer Vision: A Survey](https://mlsanity.com/paper/2309.09473/) (2023)
- [Discriminative Anchor Learning for Efficient Multi-view Clustering](https://mlsanity.com/paper/2409.16904/) (2024)
- [Dual-Optimized Adaptive Graph Reconstruction for Multi-View Graph Clustering](https://mlsanity.com/paper/2410.22983/) (2024)
- [Preventing Dimensional Collapse of Incomplete Multi-View Clustering via Direct Contrastive Learning](https://mlsanity.com/paper/2303.12241/) (2023)
- [Multi-Task Curriculum Graph Contrastive Learning with Clustering Entropy Guidance](https://mlsanity.com/paper/2408.12071/) (2024)
- [Joint Tensor and Inter-View Low-Rank Recovery for Incomplete Multiview Clustering](https://mlsanity.com/paper/2503.02449/) (2025)
- [Dual Consistent Constraint via Disentangled Consistency and Complementarity for Multi-view Clustering](https://mlsanity.com/paper/2504.04676/) (2025)
- [Two-Stage Feature Generation with Transformer and Reinforcement Learning](https://mlsanity.com/paper/2505.21978/) (2025)
- [LargeMvC-Net: Anchor-based Deep Unfolding Network for Large-scale Multi-view Clustering](https://mlsanity.com/paper/2507.20980/) (2025)
- [Missing Pattern Tree based Decision Grouping and Ensemble for Enhancing Pair Utilization in Deep Incomplete Multi-View Clustering](https://mlsanity.com/paper/2512.21510/) (2025)
- [Global-Graph Guided and Local-Graph Weighted Contrastive Learning for Unified Clustering on Incomplete and Noise Multi-View Data](https://mlsanity.com/paper/2512.21516/) (2025)
- [Beyond Missing Rates: Rethinking Incomplete Multi-View Clustering with Protocol Divergence](https://mlsanity.com/paper/2606.04857/) (2026)
- [SPORT: Structure-Aware Prototype Disentanglement for Incomplete Multi-View Clustering](https://mlsanity.com/paper/2607.10413/) (2026)

## Abstract (arXiv)

Nowadays, with the rapid development of data collection sources and feature extraction methods, multi-view data are getting easy to obtain and have received increasing research attention in recent years, among which, multi-view clustering (MVC) forms a mainstream research direction and is widely used in data analysis. However, existing MVC methods mainly assume that each sample appears in all the views, without considering the incomplete view case due to data corruption, sensor failure, equipment malfunction, etc. In this study, we design and build a generative partial multi-view clustering model, named as GP-MVC, to address the incomplete multi-view problem by explicitly generating the data of missing views. The main idea of GP-MVC lies at two-fold. First, multi-view encoder networks are trained to learn common low-dimensional representations, followed by a clustering layer to capture the consistent cluster structure across multiple views. Second, view-specific generative adversarial networks are developed to generate the missing data of one view conditioning on the shared representation given by other views. These two steps could be promoted mutually, where learning common representations facilitates data imputation and the generated data could further explores the view consistency. Moreover, an weighted adaptive fusion scheme is implemented to exploit the complementary information among different views. Experimental results on four benchmark datasets are provided to show the effectiveness of the proposed GP-MVC over the state-of-the-art methods.