mlsanityData to 7 Oct 2026

A Survey on Incomplete Multi-view Clustering

arXiv 2208.08040 ↗First posted 17 Aug 2022cs.LG cs.MMcited by 31

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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.

Key facts. Cited by 31 papers in the map (data through 7 Oct 2026). Builds on 11 of its 63 references that are in the map. First posted on arXiv 17 Aug 2022.

Builds on

11 of 63 references.

References · 11

  1. 2013A Survey on Multi-view Learningcited by 152
  2. 2021Multi-view Contrastive Graph Clusteringcited by 76
  3. 2019Doubly Aligned Incomplete Multi-view Clusteringcited by 40
  4. 2017VIGAN: Missing View Imputation with Generative Adversarial Networkscited by 33
  5. 2021Structured Graph Learning for Scalable Subspace Clustering: From Single-view to Multi-viewcited by 30
  6. 2019Multi-graph Fusion for Multi-view Spectral Clusteringcited by 27
  7. 2019One-Pass Incomplete Multi-view Clusteringcited by 24
  8. 2019Spectral Perturbation Meets Incomplete Multi-view Datacited by 24
  9. 2018Incomplete Multi-view Clustering via Graph Regularized Matrix Factorizationcited by 15
  10. 2013Clustering on Multiple Incomplete Datasets via Collective Kernel Learningcited by 11
  11. 2016Multiview Cauchy Estimator Feature Embedding for Depth and Inertial Sensor-Based Human Action Recognitioncited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 8; 2024: 11; 2025: 7; 2026: 5.

31 papers.

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  1. 2024Multimodal Fusion on Low-quality Data: A Comprehensive Surveycited by 48
  2. 2024Reliable Conflictive Multi-View Learningcited by 45
  3. 2023Incomplete Multi-View Multi-Label Learning via Label-Guided Masked View- and Category-Aware Transformerscited by 17
  4. 2023Information Recovery-Driven Deep Incomplete Multiview Clustering Networkcited by 16
  5. 2023Incomplete Contrastive Multi-View Clustering with High-Confidence Guidingcited by 15
  6. 2023DICNet: Deep Instance-Level Contrastive Network for Double Incomplete Multi-View Multi-Label Classificationcited by 13
  7. 2023Reliable Representation Learning for Incomplete Multi-View Missing Multi-Label Classificationcited by 12
  8. 2024Trusted Multi-view Learning under Noisy Supervisioncited by 8
  9. 2026Rethinking Weakly-supervised Video Temporal Grounding From a Game Perspectivecited by 8
  10. 2023Multi-view MERA Subspace Clusteringcited by 7
  11. 2024Masked Two-channel Decoupling Framework for Incomplete Multi-view Weak Multi-label Learningcited by 7
  12. 2023Joint Projection Learning and Tensor Decomposition Based Incomplete Multi-view Clusteringcited by 5
19 more
  1. 2024Manifold-based Incomplete Multi-view Clustering via Bi-Consistency Guidancecited by 5
  2. 2026Fewer Steps, Better Performance: Efficient Cross-Modal Clip Trimming for Video Moment Retrieval Using Languagecited by 5
  3. 2025Incomplete Graph Learning: A Comprehensive Surveycited by 3
  4. 2024URRL-IMVC: Unified and Robust Representation Learning for Incomplete Multi-View Clusteringcited by 2
  5. 2023Self-supervised Multi-view Clustering in Computer Vision: A Surveycited by 1
  6. 2024DTization: A New Method for Supervised Feature Scalingcited by 1
  7. 2024Task-Augmented Cross-View Imputation Network for Partial Multi-View Incomplete Multi-Label Classificationcited by 1
  8. 2025Reliable Disentanglement Multi-view Learning Against View Adversarial Attackscited by 1
  9. 2025Advanced Unsupervised Learning: A Comprehensive Overview of Multi-View Clustering Techniquescited by 1
  10. 2024Cluster Quilting: Spectral Clustering for Patchwork Learningcited by 0
  11. 2024Active-Passive Federated Learning for Vertically Partitioned Multi-view Datacited by 0
  12. 2024TPCH: Tensor-interacted Projection and Cooperative Hashing for Multi-view Clusteringcited by 0
  13. 2025Joint Tensor and Inter-View Low-Rank Recovery for Incomplete Multiview Clusteringcited by 0
  14. 2025An Interdisciplinary and Cross-Task Review on Missing Data Imputationcited by 0
  15. 2025MCFCN: Multi-View Clustering via a Fusion-Consensus Graph Convolutional Networkcited by 0
  16. 2025Missing Pattern Tree based Decision Grouping and Ensemble for Enhancing Pair Utilization in Deep Incomplete Multi-View Clusteringcited by 0
  17. 2026Adaptive Disentangled Representation Learning for Incomplete Multi-View Multi-Label Classificationcited by 0
  18. 2026Beyond Missing Rates: Rethinking Incomplete Multi-View Clustering with Protocol Divergencecited by 0
  19. 2026When Semantically Consistent Encoding Meets View-Label Heterogeneity Modeling: A Unified Framework for Incomplete Multi-View Multi-Label Learningcited by 0