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 [email protected].

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

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]

Deep Incomplete Multi-view Clustering

1. NeurIPS2019: CPM-Nets: Cross Partial Multi-View Networks Paper code
2. TIP2020: Generative Partial Multi-View Clustering paper code
3. CVPR2021: COMPLETER: Incomplete Multi-view Clustering via Contrastive Prediction Papercode
4. TIP2021: iCmSC: Incomplete Cross-modal Subspace Clustering paper code
5. TPAMI2022: Deep Partial Multi-View Learning paper code
6. TPAMI2022: Dual Contrastive Prediction for Incomplete Multi-view Representation Learning 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

Kernelized Datasets

1. The following kernelized datasets are created by our team. For more information, you can ask [email protected] 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}
}