/awesome-multi-view-clustering

collections for advanced, novel multi-view clustering methods(papers , codes and datasets)

awesome multi-view clustering

Collections for state-of-the-art (SOTA), novel multi-view clustering methods (papers, codes and datasets)

We are looking forward for other participants to share their papers and codes. If interested, please contanct wangsiwei13@nudt.edu.cn.

Table of Contents


Important Survey Papers

  1. A survey on multi-view learning Paper

  2. A study of graph-based system for multi-view clustering Paper code

  3. Multi-view clustering: A survey Paper

  4. Multi-view learning overview: Recent progress and new challenges Paper


Papers

Papers are listed in the following methods:graph clustering, NMF-based clustering, co-regularized, subspace clustering and multi-kernel clustering

Graph Clusteirng

  1. AAAI15: Large-Scale Multi-View Spectral Clustering via Bipartite Graph Paper code

  2. IJCAI17: Self-Weighted Multiview Clustering with Multiple Graphs" Paper code

  3. TKDE2018: One-step multi-view spectral clustering Paper code

  4. TKDE19: GMC: Graph-based Multi-view Clustering Paper code

  5. ICDM2019: Consistency Meets Inconsistency: A Unified Graph Learning Framework for Multi-view Clustering Paper code

  6. TMM 2021: Consensus Graph Learning for Multi-view Clustering code

Multiple Kernel Clustering(MKC)

  1. NIPS14: Localized Data Fusion for Kernel k-Means Clustering with Application to Cancer Biology Paper code

  2. IJCAI15: Robust Multiple Kernel K-means using L21-norm Paper code

  3. AAAI16:Multiple Kernel k-Means Clustering with Matrix-Induced Regularization Paper code

  4. IJCAI19: Multi-view Clustering with Late Fusion Alignment Maximization Paper code

  5. TNNLS2019: Multiple kernel clustering with neighbor-kernel subspace segmentation Paper code

Subspace Clustering

  1. CVPR2015 Diversity-induced Multi-view Subspace Clustering Paper code

  2. CVPR2017 Latent Multi-view Subspace Clustering Paper code

  3. AAAI2018 Consistent and Specific Multi-view Subspace Clustering Paper code

  4. PR2018: Multi-view Low-rank Sparse Subspace Clustering Paper code

  5. TIP2019: Split Multiplicative Multi-view Subspace Clustering Paper code

  6. IJCAI19: Flexible multi-view representation learning for subspace clustering Paper code

  7. ICCV19: Reciprocal Multi-Layer Subspace Learning for Multi-View Clustering Paper code

Deep Multi-view Clustering

  1. TPAMI 2018: Generalized Latent Multi-View Subspace Clustering(gLMSC)[Paper] [Code]

  2. STSP 2018: Deep Multimodal Subspace Clustering Networks(DMSC)[Paper] [Code]

  3. CVPR 2019: AE^2-Nets: Autoencoder in Autoencoder Networks(AE^2-Nets)[Paper] [Code]

  4. ICML 2019: COMIC: Multi-view Clustering Without Parameter Selection(COMIC)[Paper] [Code]

  5. IJCAI 2019: Deep Adversarial Multi-view Clustering Network(DAMC)[Paper] [Code]

  6. IJCAI 2019: Multi-view Spectral Clustering Network(MvSCN)[Paper] [Code]

  7. TIP 2019: Multi-view Deep Subspace Clustering Networks(MvDSCN)[Paper] [Code]

  8. AAAI 2020: Cross-modal Subspace Clustering via Deep Canonical Correlation Analysis(CMSC-DCCA)[Paper]

  9. AAAI 2020: Shared Generative Latent Representation Learning for Multi-View Clustering(DMVCVAE)[Paper] [Code]

  10. CVPR 2020: End-to-End Adversarial-Attention Network for Multi-Modal Clustering(EAMC)[Paper] [Code]

  11. IJCAI 2020: Multi-View Attribute Graph Convolution Networks for Clustering(MAGCN)[Paper] [Code]

  12. IS 2020: Deep Embedded Multi-view Clustering with Collaborative Training(DEMVC)[Paper] [Code]

  13. TKDE 2020: Joint Deep Multi-View Learning for Image Clustering(DMJC)[Paper]

  14. WWW 2020: One2Multi Graph Autoencoder for Multi-view Graph Clustering(O2MAC)[Paper] [Code]

  15. AAAI 2021: Deep Mutual Information Maximin for Cross-Modal Clustering(DMIM)[Paper]

  16. CVPR 2021: Reconsidering Representation Alignment for Multi-view Clustering(SiMVC&CoMVC)[Paper] [Code]

  17. DSE 2021: Deep Multiple Auto-Encoder-Based Multi-view Clustering(MVC_MAE)[Paper] [Code]

  18. ICCV 2021: Multimodal Clustering Networks for Self-supervised Learning from Unlabeled Videos(MCN)[Paper] [Code]

  19. ICCV 2021: Multi-VAE: Learning Disentangled View-common and View-peculiar Visual Representations for Multi-view Clustering(Multi-VAE)[Paper] [Code]

  20. IJCAI 2021: Graph Filter-based Multi-view Attributed Graph Clustering(MvAGC)[Paper] [Code]

  21. Neurcom 2021: Multi-view Subspace Clustering Networks with Local and Global Graph Information(MSCNGL)[Paper] [Code]

  22. NeurIPS 2021: Multi-view Contrastive Graph Clustering(MCGC)[Paper] [Code]

  23. TKDE 2021: Self-supervised Discriminative Feature Learning for Deep Multi-view Clustering(SDMVC)[Paper] [Code]

  24. TKDE 2021: Multi-view Attributed Graph Clustering(MAGC)[Paper] [Code]

  25. TMM 2021: Deep Multi-view Subspace Clustering with Unified and Discriminative Learning(DMSC-UDL)[Paper] [Code]

  26. TMM 2021: Self-supervised Graph Convolutional Network for Multi-view Clustering(SGCMC)[Paper] [Code]

  27. TNNLS 2021: Deep Multiview Collaborative Clustering(DMCC)[Paper]

  28. TPAMI 2021: Adaptive Graph Auto-Encoder for General Data Clustering(AdaGAE)[Paper] [Code]

  29. ACMMM 2021: Consistent Multiple Graph Embedding for Multi-View Clustering(CMGEC)[Paper] [Code]

  30. AAAI 2022: Stationary Diffusion State Neural Estimation for Multiview Clustering(SDSNE)[Paper] [Code]

  31. CVPR 2022: Deep Safe Multi-View Clustering:Reducing the Risk of Clustering Performance Degradation Caused by View Increase(DSMVC)[Paper] [Code]

  32. CVPR 2022: Multi-level Feature Learning for Contrastive Multi-view Clustering(MFLVC)[Paper] [Code]

  33. IJCAI 2022: Contrastive Multi-view Hyperbolic Hierarchical Clustering(CMHHC)[Paper]

  34. NN 2022: Multi-view Graph Embedding Clustering Network:Joint Self-supervision and Block Diagonal Representation(MVGC)[Paper] [Code]

  35. IPM 2023: Joint Contrastive Triple-learning for Deep Multi-view Clustering(JCT)[Paper] [Code]

  36. 2023: Tensorized Adaptive Deep Multi-view Subspace Clustering[Code]

Deep Incomplete Multi-view Clustering

  1. NeurIPS 2019: CPM-Nets: Cross Partial Multi-View Networks[Paper] [Code]
  2. TIP 2020: Generative Partial Multi-View Clustering[Paper] [Code]
  3. CVPR 2021: COMPLETER: Incomplete Multi-view Clustering via Contrastive Prediction[Paper] [Code]
  4. TIP 2021: iCmSC: Incomplete Cross-modal Subspace Clustering[Paper] [Code]
  5. TPAMI 2022: Deep Partial Multi-View Learning[Paper] [Code]
  6. TPAMI 2022: Dual Contrastive Prediction for Incomplete Multi-view Representation Learning[Paper] [Code]
  7. ICML 2022: Deep Safe Incomplete Multi-view Clustering: Theorem and Algorithm[Paper] [Code]

Binary Multi-view Clustering

  1. TPAMI2019: Binary Multi-View Clustering Paper code

NMF-based Multi-view Clustering

  1. AAAI20: Multi-view Clustering in Latent Embedding Space Paper code

Ensemble-based Multi-view Clustering

  1. TNNLS2019: Marginalized Multiview Ensemble Clustering Paper code

Scalable Multi-view Clustering

  1. TPAMI 2021: Multi-view Clustering: A Scalable and Parameter-free Bipartite Graph Fusion Method Paper code fvnh

  2. AAAI20: Large-scale Multi-view Subspace Clustering in Linear Time paper code

  3. ACM MM2021: Scalable Multi-view Subspace Clustering with Unified Anchors paper code

  4. TIP22: Fast Parameter-Free Multi-View Subspace Clustering with Consensus Anchor Guidance paper code

Evolutionary Multi-view Clustering

  1. Applied Soft Computing 2021: An Evolutionary Many-objective Approach to Multiview Clustering Using Feature and Relational Data Paper code

Benchmark Datasets

Oringinal Datasets

  1. It contains seven widely-used multi-view datasets: Handwritten (HW), Caltech-7/20, BBCsports, Nuswide, ORL and Webkb. Released by Baidu Service. address (code)gaih
Name of dataset Samples Views Clusters Original location
Handwritten 2000 6 10
Caltech-7 1474 6 7 http://www.vision.caltech.edu/Image_Datasets/Caltech101/
Caltech-20 2386 6 20 http://www.vision.caltech.edu/Image_Datasets/Caltech101/
BBCsports 3183 2 5 http://mlg.ucd.ie/datasets/segment.html
Nuswide 30000 5 31 https://lms.comp.nus.edu.sg/wp-content/uploads/2019/research/nuswide/NUS-WIDE.html
ORL 400 3 40 http://www.uk.research.att.com/facedatabase.html
Webkb 1051 2 2 http://www.cs.cmu.edu/afs/cs/project/theo-11/www/wwkb/ http://membres-lig.imag.fr/grimal/data.html
Cornell 165 2 15 http://membres-lig.imag.fr/grimal/data.html
MSRC-v1 210 6 7 https://www.microsoft.com/en-us/research/project/image-understanding/?from=http%3A%2F%2Fresearch.microsoft.com%2Fen-us%2Fprojects%2Fobjectclassrecognition%2F
Wikipedia 693 2 10 http://www.svcl.ucsd.edu/projects/crossmodal/
BBCsport 116 4 5 http://mlg.ucd.ie/datasets/segment.html http://mlg.ucd.ie/datasets/bbc.html
yaleA 165 3 15 http://www.cad.zju.edu.cn/home/dengcai/Data/FaceData.html
mfeat 2000 6 10 http://archive.ics.uci.edu/ml/datasets/Multiple+Features
aloi 110250 8 1000 http://elki.dbs.ifi.lmu.de/wiki/DataSets/MultiView

Kernelized Datasets

  1. The following kernelized datasets are created by our team. For more information, you can ask wangsiwei13@nudt.edu.cn for help. address (code)y44e

If you use our code or datasets, please cite our with the following bibtex code :

@inproceedings{wang2019multi,
  title={Multi-view clustering via late fusion alignment maximization},
  author={Wang, Siwei and Liu, Xinwang and Zhu, En and Tang, Chang and Liu, Jiyuan and Hu, Jingtao and Xia, Jingyuan and Yin, Jianping},
  booktitle={Proceedings of the 28th International Joint Conference on Artificial Intelligence},
  pages={3778--3784},
  year={2019},
  organization={AAAI Press}
}