Awesome

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

• Surveys
• Papers and Codes
  • Graph Clustering
  • Multiple Kenrel Clustering (MKC)
  • Subspace Clustering
  • NMF-based Clustering
  • Deep Multi-view Clustering
  • Binary Multi-view Clustering
  • Ensemble Multi-view Clustering
  • Scalable Multi-view Clustering
  • Evolutionary Multi-view Clustering
• Benchmark Datasets
  • Oringinal Datasets
  • Kernelized Datasets

<span id="jump1">Important Survey Papers </span>

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

<span id="jump2">Papers </span>

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

<span id="jump21">Graph Clusteirng</span>

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

<span id="jump22">Multiple Kernel Clustering(MKC)</span>

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

<span id="jump23">Subspace Clustering</span>

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

<span id="jump24">Deep Multi-view Clustering</span>

1. TPAMI 2018: Generalized Latent Multi-View Subspace Clustering(gLMSC)[<a href= "http://cic.tju.edu.cn/faculty/huqinghua/pdf/GeneralizedLatentMulti-ViewSubspaceClustering.pdf" target="_blank">Paper</a>] [<a href="http://cic.tju.edu.cn/faculty/zhangchangqing/code.html" target="_blank">Code</a>]

2. STSP 2018: Deep Multimodal Subspace Clustering Networks(DMSC)[<a href= "https://arxiv.org/pdf/1804.06498.pdf" target="_blank">Paper</a>] [<a href="https://github.com/mahdiabavisani/Deep-multimodal-subspace-clustering-networks" target="_blank">Code</a>]

3. CVPR 2019: AE^2-Nets: Autoencoder in Autoencoder Networks(AE^2-Nets)[<a href= "http://cic.tju.edu.cn/faculty/zhangchangqing/pub/AE2_Nets.pdf" target="_blank">Paper</a>] [<a href="https://github.com/willow617/AE2-Nets" target="_blank">Code</a>]

4. ICML 2019: COMIC: Multi-view Clustering Without Parameter Selection(COMIC)[<a href= "http://proceedings.mlr.press/v97/peng19a/peng19a.pdf" target="_blank">Paper</a>] [<a href="https://github.com/limit-scu/2019-ICML-COMIC" target="_blank">Code</a>]

5. IJCAI 2019: Deep Adversarial Multi-view Clustering Network(DAMC)[<a href= "https://www.researchgate.net/publication/334844473_Deep_Adversarial_Multi-view_Clustering_Network" target="_blank">Paper</a>] [<a href="https://github.com/IMKBLE/DAMC" target="_blank">Code</a>]

6. IJCAI 2019: Multi-view Spectral Clustering Network(MvSCN)[<a href= "https://www.ijcai.org/Proceedings/2019/0356.pdf">Paper</a>] [<a href="https://github.com/limit-scu/2019-IJCAI-MvSCN" target="_blank">Code</a>]

7. TIP 2019: Multi-view Deep Subspace Clustering Networks(MvDSCN)[<a href= "https://arxiv.org/abs/1908.01978" target="_blank">Paper</a>] [<a href="https://github.com/huybery/MvDSCN" target="_blank">Code</a>]

8. AAAI 2020: Cross-modal Subspace Clustering via Deep Canonical Correlation Analysis(CMSC-DCCA)[<a href= "https://ojs.aaai.org/index.php/AAAI/article/view/5808/5664" target="_blank">Paper</a>]

9. AAAI 2020: Shared Generative Latent Representation Learning for Multi-View Clustering(DMVCVAE)[<a href= "https://ojs.aaai.org/index.php/AAAI/article/download/6146/6002" target="_blank">Paper</a>] [<a href="https://github.com/whytin95/DMVCVAE" target="_blank">Code</a>]

10. CVPR 2020: End-to-End Adversarial-Attention Network for Multi-Modal Clustering(EAMC)[<a href= "https://openaccess.thecvf.com/content_CVPR_2020/papers/Zhou_End-to-End_Adversarial-Attention_Network_for_Multi-Modal_Clustering_CVPR_2020_paper.pdf" target="_blank">Paper</a>] [<a href="https://github.com/AllenWrong/mvc" target="_blank">Code</a>]

11. IJCAI 2020: Multi-View Attribute Graph Convolution Networks for Clustering(MAGCN)[<a href= "https://www.ijcai.org/proceedings/2020/0411.pdf" target="_blank">Paper</a>] [<a href="https://github.com/IMKBLE/MAGCN" target="_blank">Code</a>]

12. IS 2020: Deep Embedded Multi-view Clustering with Collaborative Training(DEMVC)[<a href= "https://arxiv.org/pdf/2007.13067.pdf" target="_blank">Paper</a>] [<a href="https://github.com/SubmissionsIn/DEMVC" target="_blank">Code</a>]

13. TKDE 2020: Joint Deep Multi-View Learning for Image Clustering(DMJC)[<a href= "https://ieeexplore.ieee.org/abstract/document/8999493/" target="_blank">Paper</a>]

14. WWW 2020: One2Multi Graph Autoencoder for Multi-view Graph Clustering(O2MAC)[<a href= "http://shichuan.org/doc/83.pdf" target="_blank">Paper</a>] [<a href="https://github.com/googlebaba/WWW2020-O2MAC" target="_blank">Code</a>]

15. AAAI 2021: Deep Mutual Information Maximin for Cross-Modal Clustering(DMIM)[<a href= "https://ojs.aaai.org/index.php/AAAI/article/view/17076/16883" target="_blank">Paper</a>]

16. CVPR 2021: Reconsidering Representation Alignment for Multi-view Clustering(SiMVC&CoMVC)[<a href= "https://openaccess.thecvf.com/content/CVPR2021/papers/Trosten_Reconsidering_Representation_Alignment_for_Multi-View_Clustering_CVPR_2021_paper.pdf" target="_blank">Paper</a>] [<a href="https://github.com/AllenWrong/mvc" target="_blank">Code</a>]

17. DSE 2021: Deep Multiple Auto-Encoder-Based Multi-view Clustering(MVC_MAE)[<a href= "https://link.springer.com/article/10.1007/s41019-021-00159-z" target="_blank">Paper</a>] [<a href="https://github.com/dugzzuli/Deep-Multiple-Auto-Encoder-Based-Multi-view-Clustering" target="_blank">Code</a>]

18. ICCV 2021: Multimodal Clustering Networks for Self-supervised Learning from Unlabeled Videos(MCN)[<a href= "https://openaccess.thecvf.com/content/ICCV2021/papers/Chen_Multimodal_Clustering_Networks_for_Self-Supervised_Learning_From_Unlabeled_Videos_ICCV_2021_paper.pdf" target="_blank">Paper</a>] [<a href="https://github.com/brian7685/Multimodal-Clustering-Network" target="_blank">Code</a>]

19. ICCV 2021: Multi-VAE: Learning Disentangled View-common and View-peculiar Visual Representations for Multi-view Clustering(Multi-VAE)[<a href= "https://openaccess.thecvf.com/content/ICCV2021/papers/Xu_Multi-VAE_Learning_Disentangled_View-Common_and_View-Peculiar_Visual_Representations_for_Multi-View_ICCV_2021_paper.pdf" target="_blank">Paper</a>] [<a href="https://github.com/SubmissionsIn/Multi-VAE" target="_blank">Code</a>]

20. IJCAI 2021: Graph Filter-based Multi-view Attributed Graph Clustering(MvAGC)[<a href= "https://www.ijcai.org/proceedings/2021/0375.pdf" target="_blank">Paper</a>] [<a href="https://github.com/sckangz/MvAGC" target="_blank">Code</a>]

21. Neurcom 2021: Multi-view Subspace Clustering Networks with Local and Global Graph Information(MSCNGL)[<a href= "https://arxiv.53yu.com/pdf/2010.09323" target="_blank">Paper</a>] [<a href="https://github.com/qinghai-zheng/MSCNLG" target="_blank">Code</a>]

22. NeurIPS 2021: Multi-view Contrastive Graph Clustering(MCGC)[<a href= "https://proceedings.neurips.cc/paper/2021/file/10c66082c124f8afe3df4886f5e516e0-Paper.pdf" target="_blank">Paper</a>] [<a href="https://github.com/panern/mcgc" target="_blank">Code</a>]

23. TKDE 2021: Self-supervised Discriminative Feature Learning for Deep Multi-view Clustering(SDMVC)[<a href= "https://arxiv.org/pdf/2103.15069.pdf" target="_blank">Paper</a>] [<a href="https://github.com/SubmissionsIn/SDMVC" target="_blank">Code</a>]

24. TKDE 2021: Multi-view Attributed Graph Clustering(MAGC)[<a href= "https://www.researchgate.net/publication/353747180_Multi-view_Attributed_Graph_Clustering" target="_blank">Paper</a>] [<a href="https://github.com/sckangz/MAGC" target="_blank">Code</a>]

25. TMM 2021: Deep Multi-view Subspace Clustering with Unified and Discriminative Learning(DMSC-UDL)[<a href= "https://ieeexplore.ieee.org/abstract/document/9204408/" target="_blank">Paper</a>] [<a href="https://github.com/IMKBLE/DMSC-UDL" target="_blank">Code</a>]

26. TMM 2021: Self-supervised Graph Convolutional Network for Multi-view Clustering(SGCMC)[<a href= "https://ieeexplore.ieee.org/abstract/document/9472979/" target="_blank">Paper</a>] [<a href="https://github.com/xdweixia/SGCMC" target="_blank">Code</a>]

27. TNNLS 2021: Deep Multiview Collaborative Clustering(DMCC)[<a href= "https://see.xidian.edu.cn/faculty/chdeng/Welcome%20to%20Cheng%20Deng's%20Homepage_files/Papers/Journal/TNNLS2021_Xu.pdf" target="_blank">Paper</a>]

28. TPAMI 2021: Adaptive Graph Auto-Encoder for General Data Clustering(AdaGAE)[<a href= "https://ieeexplore.ieee.org/document/9606581" target="_blank">Paper</a>] [<a href="https://github.com/hyzhang98/AdaGAE" target="_blank">Code</a>]

29. ACMMM 2021: Consistent Multiple Graph Embedding for Multi-View Clustering(CMGEC)[<a href= "https://arxiv.org/pdf/2105.04880.pdf" target="_blank">Paper</a>] [<a href="https://github.com/wangemm/CMGEC" target="_blank">Code</a>]

30. AAAI 2022: Stationary Diffusion State Neural Estimation for Multiview Clustering(SDSNE)[<a href= "https://www.aaai.org/AAAI22Papers/AAAI-184.LiuC.pdf" target="_blank">Paper</a>] [<a href="https://github.com/kunzhan/SDSNE" target="_blank">Code</a>]

31. CVPR 2022: Deep Safe Multi-View Clustering：Reducing the Risk of Clustering Performance Degradation Caused by View Increase(DSMVC)[<a href= "https://openaccess.thecvf.com/content/CVPR2022/papers/Tang_Deep_Safe_Multi-View_Clustering_Reducing_the_Risk_of_Clustering_Performance_CVPR_2022_paper.pdf" target="_blank">Paper</a>] [<a href="https://github.com/Gasteinh/DSMVC" target="_blank">Code</a>]

32. CVPR 2022: Multi-level Feature Learning for Contrastive Multi-view Clustering(MFLVC)[<a href= "https://openaccess.thecvf.com/content/CVPR2022/papers/Xu_Multi-Level_Feature_Learning_for_Contrastive_Multi-View_Clustering_CVPR_2022_paper.pdf" target="_blank">Paper</a>] [<a href="https://github.com/SubmissionsIn/MFLVC" target="_blank">Code</a>]

33. IJCAI 2022: Contrastive Multi-view Hyperbolic Hierarchical Clustering(CMHHC)[<a href= "https://arxiv.org/pdf/2205.02618.pdf" target="_blank">Paper</a>]

34. NN 2022: Multi-view Graph Embedding Clustering Network：Joint Self-supervision and Block Diagonal Representation(MVGC)[<a href= "https://www.sciencedirect.com/science/article/pii/S089360802100397X" target="_blank">Paper</a>] [<a href="https://github.com/xdweixia/NN-2022-MVGC" target="_blank">Code</a>]

35. IPM 2023: Joint Contrastive Triple-learning for Deep Multi-view Clustering(JCT)[<a href= "https://www.sciencedirect.com/science/article/abs/pii/S0306457323000213" target="_blank">Paper</a>] [<a href="https://github.com/ShizheHu/Joint-Contrastive-Triple-learning" target="_blank">Code</a>]

36. 2023: Tensorized Adaptive Deep Multi-view Subspace Clustering[<a href="https://github.com/YanghangZheng-GDUT/Tensorized-Adaptive-Deep-Multi-view-Subspace-Clustering" target="_blank">Code</a>]

<span id="jump24">Deep Incomplete Multi-view Clustering</span>

1. NeurIPS 2019: CPM-Nets: Cross Partial Multi-View Networks[<a href= "https://papers.nips.cc/paper/2019/file/11b9842e0a271ff252c1903e7132cd68-Paper.pdf" target="_blank">Paper</a>] [<a href="https://github.com/hanmenghan/CPM_Nets" target="_blank">Code</a>]
2. TIP 2020: Generative Partial Multi-View Clustering[<a href= "https://arxiv.org/abs/2003.13088" target="_blank">Paper</a>] [<a href="https://github.com/IMKBLE/PVC-GAN" target="_blank">Code</a>]
3. CVPR 2021: COMPLETER: Incomplete Multi-view Clustering via Contrastive Prediction[<a href= "http://pengxi.me/wp-content/uploads/2021/03/2021CVPR-completer.pdf" target="_blank">Paper</a>] [<a href="https://github.com/XLearning-SCU/2021-CVPR-Completer" target="_blank">Code</a>]
4. TIP 2021: iCmSC: Incomplete Cross-modal Subspace Clustering[<a href= "https://ieeexplore.ieee.org/abstract/document/9259207" target="_blank">Paper</a>] [<a href="https://github.com/IMKBLE/iCmSC" target="_blank">Code</a>]
5. TPAMI 2022: Deep Partial Multi-View Learning[<a href= "https://arxiv.org/abs/2011.06170" target="_blank">Paper</a>] [<a href="https://github.com/IMKBLE/DAMC" target="_blank">Code</a>]
6. TPAMI 2022: Dual Contrastive Prediction for Incomplete Multi-view Representation Learning[<a href= "http://pengxi.me/wp-content/uploads/2022/08/DCP.pdf" target="_blank">Paper</a>] [<a href="https://github.com/XLearning-SCU/2021-CVPR-Completer" target="_blank">Code</a>]
7. ICML 2022: Deep Safe Incomplete Multi-view Clustering: Theorem and Algorithm[<a href= "https://proceedings.mlr.press/v162/tang22c/tang22c.pdf" target="_blank">Paper</a>] [<a href="https://github.com/Gasteinh/DSIMVC" target="_blank">Code</a>]

<span id="jump25">Binary Multi-view Clustering</span>

1. TPAMI2019: Binary Multi-View Clustering Paper code

<span id="jump26">NMF-based Multi-view Clustering</span>

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

<span id="jump27"> Ensemble-based Multi-view Clustering</span>

1. TNNLS2019: Marginalized Multiview Ensemble Clustering Paper code

<span id="jump28"> Scalable Multi-view Clustering</span>

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

<span id="jump9"> Evolutionary Multi-view Clustering</span>

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

<span id="jump3">Benchmark Datasets</span>

<span id="jump31">Oringinal Datasets</span>

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

<span id="jump32">Kernelized Datasets</span>

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}
}