mlsanityData to 14 Oct 2026

Fast Continual Multi-View Clustering with Incomplete Views

arXiv 2306.02389 ↗First posted 4 Jun 2023cs.LG cs.AIcited by 19

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Abstract (arXiv)

Multi-view clustering (MVC) has gained broad attention owing to its capacity to exploit consistent and complementary information across views. This paper focuses on a challenging issue in MVC called the incomplete continual data problem (ICDP). In specific, most existing algorithms assume that views are available in advance and overlook the scenarios where data observations of views are accumulated over time. Due to privacy considerations or memory limitations, previous views cannot be stored in these situations. Some works are proposed to handle it, but all fail to address incomplete views. Such an incomplete continual data problem (ICDP) in MVC is tough to solve since incomplete information with continual data increases the difficulty of extracting consistent and complementary knowledge among views. We propose Fast Continual Multi-View Clustering with Incomplete Views (FCMVC-IV) to address it. Specifically, it maintains a consensus coefficient matrix and updates knowledge with the incoming incomplete view rather than storing and recomputing all the data matrices. Considering that the views are incomplete, the newly collected view might contain samples that have yet to appear; two indicator matrices and a rotation matrix are developed to match matrices with different dimensions. Besides, we design a three-step iterative algorithm to solve the resultant problem in linear complexity with proven convergence. Comprehensive experiments on various datasets show the superiority of FCMVC-IV.

Key facts. Cited by 19 papers in the map (data through 14 Oct 2026). Builds on 21 of its 106 references that are in the map. First posted on arXiv 4 Jun 2023.

Builds on

21 of 106 references.

References · 21

  1. 2018Continual Lifelong Learning with Neural Networks: A Reviewcited by 1,330
  2. 2021Deep Graph Clustering via Dual Correlation Reductioncited by 97
  3. 2019Large-scale Multi-view Subspace Clustering in Linear Timecited by 66
  4. 2022Simple Contrastive Graph Clusteringcited by 56
  5. 2022Knowledge Graph Contrastive Learning Based on Relation-Symmetrical Structurecited by 43
  6. 2019Doubly Aligned Incomplete Multi-view Clusteringcited by 40
  7. 2023Deep Incomplete Multi-view Clustering with Cross-view Partial Sample and Prototype Alignmentcited by 33
  8. 2023GCFAgg: Global and Cross-view Feature Aggregation for Multi-view Clusteringcited by 30
  9. 2023Deep Temporal Graph Clusteringcited by 28
  10. 2019Multi-graph Fusion for Multi-view Spectral Clusteringcited by 27
  11. 2020Generative Partial Multi-View Clusteringcited by 25
  12. 2019One-Pass Incomplete Multi-view Clusteringcited by 24
9 more
  1. 2022Mixed Graph Contrastive Network for Semi-Supervised Node Classificationcited by 21
  2. 2023Auto-weighted Multi-view Clustering for Large-scale Datacited by 20
  3. 2021Self-supervised Contrastive Attributed Graph Clusteringcited by 18
  4. 2022Local Sample-weighted Multiple Kernel Clustering with Consensus Discriminative Graphcited by 16
  5. 2020A Similarity Inference Metric for RGB-Infrared Cross-Modality Person Re-identificationcited by 14
  6. 2020Multi-View Spectral Clustering with High-Order Optimal Neighborhood Laplacian Matrixcited by 12
  7. 2021Consistent Multiple Graph Embedding for Multi-View Clusteringcited by 12
  8. 2018Multiple Kernel k-Means Clustering by Selecting Representative Kernelscited by 9
  9. 2018Robust Subspace Clustering with Compressed Datacited by 8

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Citing papers per year: 2023: 6; 2024: 4; 2025: 9.

19 papers.

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  1. 2023Unpaired Multi-View Graph Clustering with Cross-View Structure Matchingcited by 19
  2. 2023DealMVC: Dual Contrastive Calibration for Multi-view Clusteringcited by 19
  3. 2025Incomplete Multi-view Clustering via Diffusion Contrastive Generationcited by 13
  4. 2023One-step Multi-view Clustering with Diverse Representationcited by 10
  5. 2025Curve-Aware Gaussian Splatting for 3D Parametric Curve Reconstructioncited by 10
  6. 2023Scalable Incomplete Multi-View Clustering with Structure Alignmentcited by 7
  7. 2023Efficient Multi-View Graph Clustering with Local and Global Structure Preservationcited by 5
  8. 2023Contrastive Continual Multi-view Clustering with Filtered Structural Fusioncited by 4
  9. 2025Deep Incomplete Multi-view Clustering with Distribution Dual-Consistency Recovery Guidancecited by 4
  10. 2025Scalable Attribute-Missing Graph Clustering via Neighborhood Differentiationcited by 2
  11. 2024Unpaired Multi-view Clustering via Reliable View Guidancecited by 1
  12. 2024Multi-level Reliable Guidance for Unpaired Multi-view Clusteringcited by 1
7 more
  1. 2025Generalized Deep Multi-view Clustering via Causal Learning with Partially Aligned Cross-view Correspondencecited by 1
  2. 2024Probabilistically Aligned View-unaligned Clustering with Adaptive Template Selectioncited by 0
  3. 2024DWCL: Dual-Weighted Contrastive Learning for Multi-View Clusteringcited by 0
  4. 2025Divide-Then-Rule: A Cluster-Driven Hierarchical Interpolator for Attribute-Missing Graphscited by 0
  5. 2025Intra-view and Inter-view Correlation Guided Multi-view Novel Class Discoverycited by 0
  6. 2025Association and Consolidation: Evolutionary Memory-Enhanced Incremental Multi-View Clusteringcited by 0
  7. 2025Missing Pattern Tree based Decision Grouping and Ensemble for Enhancing Pair Utilization in Deep Incomplete Multi-View Clusteringcited by 0