# A Survey on Incomplete Multi-view Clustering

arXiv: https://arxiv.org/abs/2208.08040 · HTML: https://mlsanity.com/paper/2208.08040/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 63 references that are in the map.
- First posted on arXiv 17 Aug 2022.

## Builds on (most cited first, 11 of 11)

- [A Survey on Multi-view Learning](https://mlsanity.com/paper/1304.5634/) (2013)
- [Multi-view Contrastive Graph Clustering](https://mlsanity.com/paper/2110.11842/) (2021)
- [Doubly Aligned Incomplete Multi-view Clustering](https://mlsanity.com/paper/1903.02785/) (2019)
- [VIGAN: Missing View Imputation with Generative Adversarial Networks](https://mlsanity.com/paper/1708.06724/) (2017)
- [Structured Graph Learning for Scalable Subspace Clustering: From Single-view to Multi-view](https://mlsanity.com/paper/2102.07943/) (2021)
- [Multi-graph Fusion for Multi-view Spectral Clustering](https://mlsanity.com/paper/1909.06940/) (2019)
- [One-Pass Incomplete Multi-view Clustering](https://mlsanity.com/paper/1903.00637/) (2019)
- [Spectral Perturbation Meets Incomplete Multi-view Data](https://mlsanity.com/paper/1906.00098/) (2019)
- [Incomplete Multi-view Clustering via Graph Regularized Matrix Factorization](https://mlsanity.com/paper/1809.05998/) (2018)
- [Clustering on Multiple Incomplete Datasets via Collective Kernel Learning](https://mlsanity.com/paper/1310.1177/) (2013)
- [Multiview Cauchy Estimator Feature Embedding for Depth and Inertial Sensor-Based Human Action Recognition](https://mlsanity.com/paper/1608.02183/) (2016)

## Built on by (most cited first, 30 of 31)

- [Multimodal Fusion on Low-quality Data: A Comprehensive Survey](https://mlsanity.com/paper/2404.18947/) (2024)
- [Reliable Conflictive Multi-View Learning](https://mlsanity.com/paper/2402.16897/) (2024)
- [Incomplete Multi-View Multi-Label Learning via Label-Guided Masked View- and Category-Aware Transformers](https://mlsanity.com/paper/2303.07180/) (2023)
- [Information Recovery-Driven Deep Incomplete Multiview Clustering Network](https://mlsanity.com/paper/2304.00429/) (2023)
- [Incomplete Contrastive Multi-View Clustering with High-Confidence Guiding](https://mlsanity.com/paper/2312.08697/) (2023)
- [DICNet: Deep Instance-Level Contrastive Network for Double Incomplete Multi-View Multi-Label Classification](https://mlsanity.com/paper/2303.08358/) (2023)
- [Reliable Representation Learning for Incomplete Multi-View Missing Multi-Label Classification](https://mlsanity.com/paper/2303.17117/) (2023)
- [Trusted Multi-view Learning under Noisy Supervision](https://mlsanity.com/paper/2404.11944/) (2024)
- [Rethinking Weakly-supervised Video Temporal Grounding From a Game Perspective](https://mlsanity.com/paper/2605.26441/) (2026)
- [Multi-view MERA Subspace Clustering](https://mlsanity.com/paper/2305.09095/) (2023)
- [Masked Two-channel Decoupling Framework for Incomplete Multi-view Weak Multi-label Learning](https://mlsanity.com/paper/2404.17340/) (2024)
- [Joint Projection Learning and Tensor Decomposition Based Incomplete Multi-view Clustering](https://mlsanity.com/paper/2310.04038/) (2023)
- [Manifold-based Incomplete Multi-view Clustering via Bi-Consistency Guidance](https://mlsanity.com/paper/2405.10987/) (2024)
- [Fewer Steps, Better Performance: Efficient Cross-Modal Clip Trimming for Video Moment Retrieval Using Language](https://mlsanity.com/paper/2605.29793/) (2026)
- [Incomplete Graph Learning: A Comprehensive Survey](https://mlsanity.com/paper/2502.12412/) (2025)
- [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)
- [DTization: A New Method for Supervised Feature Scaling](https://mlsanity.com/paper/2404.17937/) (2024)
- [Task-Augmented Cross-View Imputation Network for Partial Multi-View Incomplete Multi-Label Classification](https://mlsanity.com/paper/2409.07931/) (2024)
- [Reliable Disentanglement Multi-view Learning Against View Adversarial Attacks](https://mlsanity.com/paper/2505.04046/) (2025)
- [Advanced Unsupervised Learning: A Comprehensive Overview of Multi-View Clustering Techniques](https://mlsanity.com/paper/2512.05169/) (2025)
- [Adaptive Disentangled Representation Learning for Incomplete Multi-View Multi-Label Classification](https://mlsanity.com/paper/2601.05785/) (2026)
- [Cluster Quilting: Spectral Clustering for Patchwork Learning](https://mlsanity.com/paper/2406.13833/) (2024)
- [Active-Passive Federated Learning for Vertically Partitioned Multi-view Data](https://mlsanity.com/paper/2409.04111/) (2024)
- [TPCH: Tensor-interacted Projection and Cooperative Hashing for Multi-view Clustering](https://mlsanity.com/paper/2412.18847/) (2024)
- [Joint Tensor and Inter-View Low-Rank Recovery for Incomplete Multiview Clustering](https://mlsanity.com/paper/2503.02449/) (2025)
- [An Interdisciplinary and Cross-Task Review on Missing Data Imputation](https://mlsanity.com/paper/2511.01196/) (2025)
- [MCFCN: Multi-View Clustering via a Fusion-Consensus Graph Convolutional Network](https://mlsanity.com/paper/2511.05554/) (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)
- [Beyond Missing Rates: Rethinking Incomplete Multi-View Clustering with Protocol Divergence](https://mlsanity.com/paper/2606.04857/) (2026)

## Abstract (arXiv)

Conventional multi-view clustering seeks to partition data into respective groups based on the assumption that all views are fully observed. However, in practical applications, such as disease diagnosis, multimedia analysis, and recommendation system, it is common to observe that not all views of samples are available in many cases, which leads to the failure of the conventional multi-view clustering methods. Clustering on such incomplete multi-view data is referred to as incomplete multi-view clustering. In view of the promising application prospects, the research of incomplete multi-view clustering has noticeable advances in recent years. However, there is no survey to summarize the current progresses and point out the future research directions. To this end, we review the recent studies of incomplete multi-view clustering. Importantly, we provide some frameworks to unify the corresponding incomplete multi-view clustering methods, and make an in-depth comparative analysis for some representative methods from theoretical and experimental perspectives. Finally, some open problems in the incomplete multi-view clustering field are offered for researchers.