# Deep Incomplete Multi-view Clustering with Cross-view Partial Sample and Prototype Alignment

arXiv: https://arxiv.org/abs/2303.15689 · HTML: https://mlsanity.com/paper/2303.15689/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 52 references that are in the map.
- First posted on arXiv 28 Mar 2023.

## Builds on (most cited first, 17 of 17)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [A Review on Generative Adversarial Networks: Algorithms, Theory, and Applications](https://mlsanity.com/paper/2001.06937/) (2020)
- [Multi-view Contrastive Graph Clustering](https://mlsanity.com/paper/2110.11842/) (2021)
- [Deep Partial Multi-View Learning](https://mlsanity.com/paper/2011.06170/) (2020)
- [Deep Fusion Clustering Network](https://mlsanity.com/paper/2012.09600/) (2020)
- [Hard Sample Aware Network for Contrastive Deep Graph Clustering](https://mlsanity.com/paper/2212.08665/) (2022)
- [Cluster-guided Contrastive Graph Clustering Network](https://mlsanity.com/paper/2301.01098/) (2023)
- [Reconsidering Representation Alignment for Multi-view Clustering](https://mlsanity.com/paper/2103.07738/) (2021)
- [Doubly Aligned Incomplete Multi-view Clustering](https://mlsanity.com/paper/1903.02785/) (2019)
- [Self-Supervised Video Hashing with Hierarchical Binary Auto-encoder](https://mlsanity.com/paper/1802.02305/) (2018)
- [Effective and Efficient Graph Learning for Multi-view Clustering](https://mlsanity.com/paper/2108.06734/) (2021)
- [Generative Partial Multi-View Clustering](https://mlsanity.com/paper/2003.13088/) (2020)
- [Multi-view Deep Subspace Clustering Networks](https://mlsanity.com/paper/1908.01978/) (2019)
- [Align then Fusion: Generalized Large-scale Multi-view Clustering with Anchor Matching Correspondences](https://mlsanity.com/paper/2205.15075/) (2022)
- [Incomplete Multi-view Clustering via Cross-view Relation Transfer](https://mlsanity.com/paper/2112.00739/) (2021)
- [Multiple Kernel Clustering with Dual Noise Minimization](https://mlsanity.com/paper/2207.06041/) (2022)

## Built on by (most cited first, 30 of 33)

- [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)
- [DealMVC: Dual Contrastive Calibration for Multi-view Clustering](https://mlsanity.com/paper/2308.09000/) (2023)
- [Reinforcement Graph Clustering with Unknown Cluster Number](https://mlsanity.com/paper/2308.06827/) (2023)
- [Hunting Attributes: Context Prototype-Aware Learning for Weakly Supervised Semantic Segmentation](https://mlsanity.com/paper/2403.07630/) (2024)
- [Incomplete Multi-view Clustering via Diffusion Contrastive Generation](https://mlsanity.com/paper/2503.09185/) (2025)
- [TMac: Temporal Multi-Modal Graph Learning for Acoustic Event Classification](https://mlsanity.com/paper/2309.11845/) (2023)
- [Deep Incomplete Multi-view Learning via Cyclic Permutation of VAEs](https://mlsanity.com/paper/2502.11037/) (2025)
- [Scalable Incomplete Multi-View Clustering with Structure Alignment](https://mlsanity.com/paper/2308.16541/) (2023)
- [Imputation-free and Alignment-free: Incomplete Multi-view Clustering Driven by Consensus Semantic Learning](https://mlsanity.com/paper/2505.11182/) (2025)
- [Efficient Multi-View Graph Clustering with Local and Global Structure Preservation](https://mlsanity.com/paper/2309.00024/) (2023)
- [Robust Multi-View Learning via Representation Fusion of Sample-Level Attention and Alignment of Simulated Perturbation](https://mlsanity.com/paper/2503.04151/) (2025)
- [Wasserstein-Aligned Hyperbolic Multi-View Clustering](https://mlsanity.com/paper/2512.09402/) (2025)
- [Multi-view Granular-ball Contrastive Clustering](https://mlsanity.com/paper/2412.13550/) (2024)
- [Learning Disentangled Representations for Generalized Multi-view Clustering](https://mlsanity.com/paper/2605.15640/) (2026)
- [Multi-level Reliable Guidance for Unpaired Multi-view Clustering](https://mlsanity.com/paper/2407.01247/) (2024)
- [Enhancing Multi-view Open-set Learning via Ambiguity Uncertainty Calibration and View-wise Debiasing](https://mlsanity.com/paper/2508.01227/) (2025)
- [RAC-DMVC: Reliability-Aware Contrastive Deep Multi-View Clustering under Multi-Source Noise](https://mlsanity.com/paper/2511.13561/) (2025)
- [Straight-Path Flow Matching for Incomplete Multi-View Clustering](https://mlsanity.com/paper/2607.06281/) (2026)
- [RetiGen: A Framework for Generalized Retinal Diagnosis Using Multi-View Fundus Images](https://mlsanity.com/paper/2403.15647/) (2024)
- [Bridging Gaps: Federated Multi-View Clustering in Heterogeneous Hybrid Views](https://mlsanity.com/paper/2410.09484/) (2024)
- [Mask-informed Deep Contrastive Incomplete Multi-view Clustering](https://mlsanity.com/paper/2502.02234/) (2025)
- [Intra-view and Inter-view Correlation Guided Multi-view Novel Class Discovery](https://mlsanity.com/paper/2507.12029/) (2025)
- [LargeMvC-Net: Anchor-based Deep Unfolding Network for Large-scale Multi-view Clustering](https://mlsanity.com/paper/2507.20980/) (2025)
- [Contrastive clustering based on regular equivalence for influential node identification in complex networks](https://mlsanity.com/paper/2509.02609/) (2025)
- [Positive Semi-definite Latent Factor Grouping-Boosted Cluster-reasoning Instance Disentangled Learning for WSI Representation](https://mlsanity.com/paper/2511.01304/) (2025)
- [Simple Yet Effective Selective Imputation for Incomplete Multi-view Clustering](https://mlsanity.com/paper/2512.10327/) (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)
- [Deep Incomplete Multi-View Clustering via Hierarchical Imputation and Alignment](https://mlsanity.com/paper/2601.09051/) (2026)
- [Quality-Aware Robust Multi-View Clustering for Heterogeneous Observation Noise](https://mlsanity.com/paper/2602.22568/) (2026)

## Abstract (arXiv)

The success of existing multi-view clustering relies on the assumption of sample integrity across multiple views. However, in real-world scenarios, samples of multi-view are partially available due to data corruption or sensor failure, which leads to incomplete multi-view clustering study (IMVC). Although several attempts have been proposed to address IMVC, they suffer from the following drawbacks: i) Existing methods mainly adopt cross-view contrastive learning forcing the representations of each sample across views to be exactly the same, which might ignore view discrepancy and flexibility in representations; ii) Due to the absence of non-observed samples across multiple views, the obtained prototypes of clusters might be unaligned and biased, leading to incorrect fusion. To address the above issues, we propose a Cross-view Partial Sample and Prototype Alignment Network (CPSPAN) for Deep Incomplete Multi-view Clustering. Firstly, unlike existing contrastive-based methods, we adopt pair-observed data alignment as 'proxy supervised signals' to guide instance-to-instance correspondence construction among views. Then, regarding of the shifted prototypes in IMVC, we further propose a prototype alignment module to achieve incomplete distribution calibration across views. Extensive experimental results showcase the effectiveness of our proposed modules, attaining noteworthy performance improvements when compared to existing IMVC competitors on benchmark datasets.