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https://github.com/wangsiwei2010/awesome-multi-view-clustering
collections for advanced, novel multi-view clustering methods(papers , codes and datasets)
https://github.com/wangsiwei2010/awesome-multi-view-clustering
List: awesome-multi-view-clustering
kernel-kmeans-clustering multi-view-clustering multi-view-data subspace-clustering
Last synced: about 1 month ago
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collections for advanced, novel multi-view clustering methods(papers , codes and datasets)
- Host: GitHub
- URL: https://github.com/wangsiwei2010/awesome-multi-view-clustering
- Owner: wangsiwei2010
- Created: 2020-04-12T14:30:38.000Z (over 4 years ago)
- Default Branch: master
- Last Pushed: 2023-03-10T09:31:12.000Z (almost 2 years ago)
- Last Synced: 2024-05-21T17:07:00.702Z (7 months ago)
- Topics: kernel-kmeans-clustering, multi-view-clustering, multi-view-data, subspace-clustering
- Homepage:
- Size: 85 KB
- Stars: 474
- Watchers: 8
- Forks: 106
- Open Issues: 8
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- awesome-machine-learning-resources - **[List - multi-view-clustering?style=social) (Table of Contents)
- ultimate-awesome - awesome-multi-view-clustering - Collections for advanced, novel multi-view clustering methods(papers , codes and datasets). (Other Lists / Monkey C Lists)
README
# 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 .
## Table of Contents
- [Surveys](#jump1)
- [Papers and Codes](#jump2)
- [Graph Clustering](#jump21)
- [Multiple Kenrel Clustering (MKC)](#jump22)
- [Subspace Clustering](#jump23)
- [NMF-based Clustering](#jump26)
- [Deep Multi-view Clustering](#jump24)
- [Binary Multi-view Clustering](#jump25)
- [Ensemble Multi-view Clustering](#jump27)
- [Scalable Multi-view Clustering](#jump28)
- [Evolutionary Multi-view Clustering](#jump29)
- [Benchmark Datasets](#jump3)
- [Oringinal Datasets](#jump31)
- [Kernelized Datasets](#jump32)
---
## Important Survey Papers
1. A survey on multi-view learning [Paper](https://arxiv.org/pdf/1304.5634)
1. A study of graph-based system for multi-view clustering [Paper](https://www.researchgate.net/profile/Hao_Wang250/publication/328573967_A_study_of_graph-based_system_for_multi-view_clustering/links/5cbff7e5299bf120977adaa6/A-study-of-graph-based-system-for-multi-view-clustering.pdf) [code](https://github.com/cswanghao/gbs)
1. Multi-view clustering: A survey [Paper](https://ieeexplore.ieee.org/iel7/8254253/8336843/08336846.pdf)
1. Multi-view learning overview: Recent progress and new challenges [Paper](https://www.researchgate.net/profile/Shiliang_Sun2/publication/314251895_Multi-view_Learning_Overview_Recent_Progress_and_New_Challenges/links/5def9d8f92851c836470978c/Multi-view-Learning-Overview-Recent-Progress-and-New-Challenges.pdf)
---
## 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](https://www.aaai.org/ocs/index.php/AAAI/AAAI15/paper/download/9641/9937) [code](https://github.com/zzz123xyz/MVSC)
1. IJCAI17: Self-Weighted Multiview Clustering with Multiple Graphs" [Paper](https://www.ijcai.org/Proceedings/2017/0357.pdf) [code](https://github.com/kylejingli/SwMC-IJCAI17)
1. TKDE2018： One-step multi-view spectral clustering [Paper](https://ieeexplore.ieee.org/abstract/document/8478288/) [code](https://pan.baidu.com/s/1eFiB87O0LBkJS8ZRSybNfQ)
1. TKDE19: GMC: Graph-based Multi-view Clustering [Paper](https://ieeexplore.ieee.org/abstract/document/8662703) [code](https://github.com/cshaowang/gmc)
1. ICDM2019: Consistency Meets Inconsistency: A Unified Graph Learning Framework for Multi-view Clustering [Paper](https://www.researchgate.net/profile/Dong_Huang9/publication/335857675_Consistency_Meets_Inconsistency_A_Unified_Graph_Learning_Framework_for_Multi-view_Clustering/links/5d809ca7458515fca16e3776/Consistency-Meets-Inconsistency-A-Unified-Graph-Learning-Framework-for-Multi-view-Clustering.pdf) [code](https://github.com/youweiliang/ConsistentGraphLearning)
1. TMM 2021: Consensus Graph Learning for Multi-view Clustering [code](https://github.com/guanyuezhen/CGL)
### Multiple Kernel Clustering(MKC)
1. NIPS14: Localized Data Fusion for Kernel k-Means Clustering with Application to Cancer Biology [Paper](https://papers.nips.cc/paper/5236-localized-data-fusion-for-kernel-k-means-clustering-with-application-to-cancer-biology.pdf) [code](https://github.com/mehmetgonen/lmkkmeans)
1. IJCAI15： Robust Multiple Kernel K-means using L21-norm [Paper](https://www.aaai.org/ocs/index.php/IJCAI/IJCAI15/paper/download/11332/11224) [code](https://github.com/csliangdu/RMKKM)
1. AAAI16：Multiple Kernel k-Means Clustering with Matrix-Induced Regularization [Paper](https://www.aaai.org/ocs/index.php/AAAI/AAAI16/paper/viewPDFInterstitial/12115/11819) [code](https://github.com/wangsiwei2010/Multiple-Kernel-k-Means-Clustering-with-Matrix-Induced-Regularization)
1. IJCAI19: Multi-view Clustering with Late Fusion Alignment Maximization [Paper](https://www.ijcai.org/proceedings/2019/0524.pdf) [code](https://github.com/wangsiwei2010/latefusionalignment)
1. TNNLS2019: Multiple kernel clustering with neighbor-kernel subspace segmentation [Paper](https://ieeexplore.ieee.org/document/8750871) [code](https://github.com/SihangZhou/Demo-of-Multiple-Kernel-Clustering-with-Neighbor-Kernel-Subspace-Segmentation)
### Subspace Clustering
1. CVPR2015 Diversity-induced Multi-view Subspace Clustering [Paper](https://www.zpascal.net/cvpr2015/Cao_Diversity-Induced_Multi-View_Subspace_2015_CVPR_paper.pdf) [code](http://cic.tju.edu.cn/faculty/zhangchangqing/code/DiMSC.rar)
1. CVPR2017 Latent Multi-view Subspace Clustering [Paper](http://cic.tju.edu.cn/faculty/zhangchangqing/pub/Zhang_Latent_Multi-View_Subspace_CVPR_2017_paper.pdf) [code](http://cic.tju.edu.cn/faculty/zhangchangqing/code/LMSC_CVPR2017_Zhang.rar)
1. AAAI2018 Consistent and Specific Multi-view Subspace Clustering [Paper](https://github.com/XIAOCHUN-CAS/Academic-Publications/blob/master/Conference/2018_AAAI_Luo.pdf) [code](https://github.com/XIAOCHUN-CAS/Consistent-and-Specific-Multi-View-Subspace-Clustering)
1. PR2018: Multi-view Low-rank Sparse Subspace Clustering [Paper](https://arxiv.org/abs/1708.08732) [code](https://github.com/wangsiwei2010/Multi-view-LRSSC)
1. TIP2019: Split Multiplicative Multi-view Subspace Clustering [Paper](https://www.researchgate.net/publication/333007034_Split_Multiplicative_Multi-view_Subspace_Clustering) [code](https://github.com/joshuaas/SM2SC)
1. IJCAI19: Flexible multi-view representation learning for subspace clustering [Paper](https://www.ijcai.org/Proceedings/2019/0404.pdf) [code](https://github.com/lslrh/FMR)
1. ICCV19: Reciprocal Multi-Layer Subspace Learning for Multi-View Clustering [Paper](http://openaccess.thecvf.com/content_ICCV_2019/papers/Li_Reciprocal_Multi-Layer_Subspace_Learning_for_Multi-View_Clustering_ICCV_2019_paper.pdf) [code](https://github.com/lslrh/RMSL)
### 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](http://cfm.uestc.edu.cn/~fshen/TPAMI-BMVC_Final.pdf) [code](https://github.com/DarrenZZhang/BMVC)
### NMF-based Multi-view Clustering
1. AAAI20: Multi-view Clustering in Latent Embedding Space [Paper](https://www.researchgate.net/profile/Dong_Huang9/publication/338883065_Multi-view_Clustering_in_Latent_Embedding_Space/links/5e30e4ee458515072d6ab048/Multi-view-Clustering-in-Latent-Embedding-Space.pdf?_sg%5B0%5D=c7_LGDqrWNZ_2R_YVqZW5paGs4aiAWHyL5Vm6D9xC-qLrwZgnT5PnHd5qcLIWLjUU1w1sMRvcFieskwMXfiUxA.C7MpmX3wox2zTGV_rHjWvJVYUcWBn5cx271Yud84FlPQiu_W8azOItQWDVbvUiM3bw4kxI_zLS8mGKTKMl5f3w&_sg%5B1%5D=Ug4z3sxpjLL5fvIFDmpbr9hht6CQIYTxXEPWuPHRJZvOOuGvEI2QyxzM8WX0M3c0SkQeyoVq3fnE9kyqH5TWHTslmLrQDWSN3t6xvMVZkLTi.C7MpmX3wox2zTGV_rHjWvJVYUcWBn5cx271Yud84FlPQiu_W8azOItQWDVbvUiM3bw4kxI_zLS8mGKTKMl5f3w&_iepl=) [code](https://github.com/Ttuo123/MCLES)
### Ensemble-based Multi-view Clustering
1. TNNLS2019: Marginalized Multiview Ensemble Clustering [Paper](https://ieeexplore.ieee.org/document/8691702) [code](https://pan.baidu.com/s/15033GUCWM5SWFlyzIkYVOg)
### Scalable Multi-view Clustering
1. TPAMI 2021: Multi-view Clustering: A Scalable and Parameter-free Bipartite Graph Fusion Method [Paper](https://ieeexplore.ieee.org/document/9146384) [code]( https://pan.baidu.com/s/1ieeDwbV8M3kCzl52bnvfnQ) fvnh
1. AAAI20: Large-scale Multi-view Subspace Clustering in Linear Time [paper](https://www.researchgate.net/publication/342540476_Large-Scale_Multi-View_Subspace_Clustering_in_Linear_Time) [code](https://github.com/sckangz/LMVSC)
1. ACM MM2021: Scalable Multi-view Subspace Clustering with Unified Anchors [paper](https://www.researchgate.net/publication/353971911_Scalable_Multi-view_Subspace_Clustering_with_Unified_Anchors) [code](https://github.com/wangsiwei2010/SMVSC)
1. TIP22: Fast Parameter-Free Multi-View Subspace Clustering with Consensus Anchor Guidance [paper](https://ieeexplore.ieee.org/document/9646486) [code](https://github.com/wangsiwei2010/FPMVS-CAG)
### Evolutionary Multi-view Clustering
1. Applied Soft Computing 2021: An Evolutionary Many-objective Approach to Multiview Clustering Using Feature and Relational Data [Paper](https://doi.org/10.1016/j.asoc.2021.107425) [code](https://github.com/adanjoga/mvmc)
---
## 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](https://pan.baidu.com/s/1hG2zL40RxVaJ_p53gBM7kA) （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 for help.
[address](https://pan.baidu.com/s/1sOpNOG_3BlNPoxhwLKbUEQ) （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}
}
```