└── README.md /README.md: -------------------------------------------------------------------------------- 1 | # awesome multi-view clustering 2 | Collections for state-of-the-art (SOTA), novel multi-view clustering methods (papers, codes and datasets) 3 | 4 | We are looking forward for other participants to share their papers and codes. If interested, please contanct . 5 | 6 | ## Table of Contents 7 | - [Surveys](#jump1) 8 | - [Papers and Codes](#jump2) 9 | - [Graph Clustering](#jump21) 10 | - [Multiple Kenrel Clustering (MKC)](#jump22) 11 | - [Subspace Clustering](#jump23) 12 | - [NMF-based Clustering](#jump26) 13 | - [Deep Multi-view Clustering](#jump24) 14 | - [Binary Multi-view Clustering](#jump25) 15 | - [Ensemble Multi-view Clustering](#jump27) 16 | - [Scalable Multi-view Clustering](#jump28) 17 | - [Evolutionary Multi-view Clustering](#jump29) 18 | - [Benchmark Datasets](#jump3) 19 | - [Oringinal Datasets](#jump31) 20 | - [Kernelized Datasets](#jump32) 21 | 22 | --- 23 | 24 | ## Important Survey Papers 25 | 1. A survey on multi-view learning [Paper](https://arxiv.org/pdf/1304.5634) 26 | 27 | 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) 28 | 29 | 1. Multi-view clustering: A survey [Paper](https://ieeexplore.ieee.org/iel7/8254253/8336843/08336846.pdf) 30 | 31 | 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) 32 | 33 | --- 34 | 35 | ## Papers 36 | Papers are listed in the following methods:graph clustering, NMF-based clustering, co-regularized, subspace clustering and multi-kernel clustering 37 | 38 | ### Graph Clusteirng 39 | 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) 40 | 41 | 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) 42 | 43 | 1. TKDE2018： One-step multi-view spectral clustering [Paper](https://ieeexplore.ieee.org/abstract/document/8478288/) [code](https://pan.baidu.com/s/1eFiB87O0LBkJS8ZRSybNfQ) 44 | 45 | 1. TKDE19: GMC: Graph-based Multi-view Clustering [Paper](https://ieeexplore.ieee.org/abstract/document/8662703) [code](https://github.com/cshaowang/gmc) 46 | 47 | 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) 48 | 49 | 1. TMM 2021: Consensus Graph Learning for Multi-view Clustering [code](https://github.com/guanyuezhen/CGL) 50 | 51 | 52 | 53 | ### Multiple Kernel Clustering(MKC) 54 | 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) 55 | 56 | 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) 57 | 58 | 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) 59 | 60 | 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) 61 | 62 | 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) 63 | 64 | ### Subspace Clustering 65 | 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) 66 | 67 | 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) 68 | 69 | 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) 70 | 71 | 1. PR2018: Multi-view Low-rank Sparse Subspace Clustering [Paper](https://arxiv.org/abs/1708.08732) [code](https://github.com/wangsiwei2010/Multi-view-LRSSC) 72 | 73 | 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) 74 | 75 | 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) 76 | 77 | 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) 78 | 79 | 80 | ### Deep Multi-view Clustering 81 | 1. TPAMI 2018: Generalized Latent Multi-View Subspace Clustering(gLMSC)[Paper] [Code] 82 | 83 | 2. STSP 2018: Deep Multimodal Subspace Clustering Networks(DMSC)[Paper] [Code] 84 | 85 | 3. CVPR 2019: AE^2-Nets: Autoencoder in Autoencoder Networks(AE^2-Nets)[Paper] [Code] 86 | 87 | 4. ICML 2019: COMIC: Multi-view Clustering Without Parameter Selection(COMIC)[Paper] [Code] 88 | 89 | 5. IJCAI 2019: Deep Adversarial Multi-view Clustering Network(DAMC)[Paper] [Code] 90 | 91 | 6. IJCAI 2019: Multi-view Spectral Clustering Network(MvSCN)[Paper] [Code] 92 | 93 | 7. TIP 2019: Multi-view Deep Subspace Clustering Networks(MvDSCN)[Paper] [Code] 94 | 95 | 8. AAAI 2020: Cross-modal Subspace Clustering via Deep Canonical Correlation Analysis(CMSC-DCCA)[Paper] 96 | 97 | 9. AAAI 2020: Shared Generative Latent Representation Learning for Multi-View Clustering(DMVCVAE)[Paper] [Code] 98 | 99 | 10. CVPR 2020: End-to-End Adversarial-Attention Network for Multi-Modal Clustering(EAMC)[Paper] [Code] 100 | 101 | 11. IJCAI 2020: Multi-View Attribute Graph Convolution Networks for Clustering(MAGCN)[Paper] [Code] 102 | 103 | 12. IS 2020: Deep Embedded Multi-view Clustering with Collaborative Training(DEMVC)[Paper] [Code] 104 | 105 | 13. TKDE 2020: Joint Deep Multi-View Learning for Image Clustering(DMJC)[Paper] 106 | 107 | 14. WWW 2020: One2Multi Graph Autoencoder for Multi-view Graph Clustering(O2MAC)[Paper] [Code] 108 | 109 | 15. AAAI 2021: Deep Mutual Information Maximin for Cross-Modal Clustering(DMIM)[Paper] 110 | 111 | 16. CVPR 2021: Reconsidering Representation Alignment for Multi-view Clustering(SiMVC&CoMVC)[Paper] [Code] 112 | 113 | 17. DSE 2021: Deep Multiple Auto-Encoder-Based Multi-view Clustering(MVC_MAE)[Paper] [Code] 114 | 115 | 18. ICCV 2021: Multimodal Clustering Networks for Self-supervised Learning from Unlabeled Videos(MCN)[Paper] [Code] 116 | 117 | 19. ICCV 2021: Multi-VAE: Learning Disentangled View-common and View-peculiar Visual Representations for Multi-view Clustering(Multi-VAE)[Paper] [Code] 118 | 119 | 20. IJCAI 2021: Graph Filter-based Multi-view Attributed Graph Clustering(MvAGC)[Paper] [Code] 120 | 121 | 21. Neurcom 2021: Multi-view Subspace Clustering Networks with Local and Global Graph Information(MSCNGL)[Paper] [Code] 122 | 123 | 22. NeurIPS 2021: Multi-view Contrastive Graph Clustering(MCGC)[Paper] [Code] 124 | 125 | 23. TKDE 2021: Self-supervised Discriminative Feature Learning for Deep Multi-view Clustering(SDMVC)[Paper] [Code] 126 | 127 | 24. TKDE 2021: Multi-view Attributed Graph Clustering(MAGC)[Paper] [Code] 128 | 129 | 25. TMM 2021: Deep Multi-view Subspace Clustering with Unified and Discriminative Learning(DMSC-UDL)[Paper] [Code] 130 | 131 | 26. TMM 2021: Self-supervised Graph Convolutional Network for Multi-view Clustering(SGCMC)[Paper] [Code] 132 | 133 | 27. TNNLS 2021: Deep Multiview Collaborative Clustering(DMCC)[Paper] 134 | 135 | 28. TPAMI 2021: Adaptive Graph Auto-Encoder for General Data Clustering(AdaGAE)[Paper] [Code] 136 | 137 | 29. ACMMM 2021: Consistent Multiple Graph Embedding for Multi-View Clustering(CMGEC)[Paper] [Code] 138 | 139 | 30. AAAI 2022: Stationary Diffusion State Neural Estimation for Multiview Clustering(SDSNE)[Paper] [Code] 140 | 141 | 31. CVPR 2022: Deep Safe Multi-View Clustering：Reducing the Risk of Clustering Performance Degradation Caused by View Increase(DSMVC)[Paper] [Code] 142 | 143 | 32. CVPR 2022: Multi-level Feature Learning for Contrastive Multi-view Clustering(MFLVC)[Paper] [Code] 144 | 145 | 33. IJCAI 2022: Contrastive Multi-view Hyperbolic Hierarchical Clustering(CMHHC)[Paper] 146 | 147 | 34. NN 2022: Multi-view Graph Embedding Clustering Network：Joint Self-supervision and Block Diagonal Representation(MVGC)[Paper] [Code] 148 | 149 | 35. IPM 2023: Joint Contrastive Triple-learning for Deep Multi-view Clustering(JCT)[Paper] [Code] 150 | 151 | 36. 2023: Tensorized Adaptive Deep Multi-view Subspace Clustering[Code] 152 | 153 | 154 | ### Deep Incomplete Multi-view Clustering 155 | 1. NeurIPS 2019: CPM-Nets: Cross Partial Multi-View Networks[Paper] [Code] 156 | 2. TIP 2020: Generative Partial Multi-View Clustering[Paper] [Code] 157 | 3. CVPR 2021: COMPLETER: Incomplete Multi-view Clustering via Contrastive Prediction[Paper] [Code] 158 | 4. TIP 2021: iCmSC: Incomplete Cross-modal Subspace Clustering[Paper] [Code] 159 | 5. TPAMI 2022: Deep Partial Multi-View Learning[Paper] [Code] 160 | 6. TPAMI 2022: Dual Contrastive Prediction for Incomplete Multi-view Representation Learning[Paper] [Code] 161 | 7. ICML 2022: Deep Safe Incomplete Multi-view Clustering: Theorem and Algorithm[Paper] [Code] 162 | 163 | ### Binary Multi-view Clustering 164 | 1. TPAMI2019: Binary Multi-View Clustering [Paper](http://cfm.uestc.edu.cn/~fshen/TPAMI-BMVC_Final.pdf) [code](https://github.com/DarrenZZhang/BMVC) 165 | 166 | 167 | ### NMF-based Multi-view Clustering 168 | 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) 169 | 170 | 171 | ### Ensemble-based Multi-view Clustering 172 | 1. TNNLS2019: Marginalized Multiview Ensemble Clustering [Paper](https://ieeexplore.ieee.org/document/8691702) [code](https://pan.baidu.com/s/15033GUCWM5SWFlyzIkYVOg) 173 | 174 | 175 | ### Scalable Multi-view Clustering 176 | 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 177 | 178 | 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) 179 | 180 | 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) 181 | 182 | 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) 183 | 184 | 185 | 186 | 187 | 188 | ### Evolutionary Multi-view Clustering 189 | 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) 190 | 191 | 192 | 193 | --- 194 | 195 | ## Benchmark Datasets 196 | ### Oringinal Datasets 197 | 1. It contains seven widely-used multi-view datasets: Handwritten (HW), Caltech-7/20, BBCsports, Nuswide, ORL and Webkb. Released by Baidu Service. 198 | [address](https://pan.baidu.com/s/1hG2zL40RxVaJ_p53gBM7kA) （code）gaih 199 | 200 | 201 | | Name of dataset | Samples | Views | Clusters | Original location | | | | 202 | |-----------------|---------|-------|----------|---------------------------------------------------------------------------------------------------------------------------------------------------------------|---------------------------------------------|---|---| 203 | | Handwritten | 2000 | 6 | 10 | | | | | 204 | | Caltech-7 | 1474 | 6 | 7 | http://www.vision.caltech.edu/Image_Datasets/Caltech101/ | | | | 205 | | Caltech-20 | 2386 | 6 | 20 | http://www.vision.caltech.edu/Image_Datasets/Caltech101/ | | | | 206 | | BBCsports | 3183 | 2 | 5 | http://mlg.ucd.ie/datasets/segment.html | | | | 207 | | Nuswide | 30000 | 5 | 31 | https://lms.comp.nus.edu.sg/wp-content/uploads/2019/research/nuswide/NUS-WIDE.html | | | | 208 | | ORL | 400 | 3 | 40 | http://www.uk.research.att.com/facedatabase.html | | | | 209 | | Webkb | 1051 | 2 | 2 | http://www.cs.cmu.edu/afs/cs/project/theo-11/www/wwkb/ | http://membres-lig.imag.fr/grimal/data.html | | | 210 | | Cornell | 165 | 2 | 15 | http://membres-lig.imag.fr/grimal/data.html | | | | 211 | | 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 | | | | 212 | | Wikipedia | 693 | 2 | 10 | http://www.svcl.ucsd.edu/projects/crossmodal/ | | | | 213 | | BBCsport | 116 | 4 | 5 | http://mlg.ucd.ie/datasets/segment.html | http://mlg.ucd.ie/datasets/bbc.html | | | 214 | | yaleA | 165 | 3 | 15 | http://www.cad.zju.edu.cn/home/dengcai/Data/FaceData.html | | | | 215 | | mfeat | 2000 | 6 | 10 | http://archive.ics.uci.edu/ml/datasets/Multiple+Features | | | | 216 | | aloi | 110250 | 8 | 1000 | http://elki.dbs.ifi.lmu.de/wiki/DataSets/MultiView | | | | 217 | 218 | ### Kernelized Datasets 219 | 1. The following kernelized datasets are created by our team. For more information, you can ask for help. 220 | [address](https://pan.baidu.com/s/1sOpNOG_3BlNPoxhwLKbUEQ) （code）y44e 221 | 222 | If you use our code or datasets, please cite our with the following bibtex code : 223 | ``` 224 | @inproceedings{wang2019multi, 225 | title={Multi-view clustering via late fusion alignment maximization}, 226 | author={Wang, Siwei and Liu, Xinwang and Zhu, En and Tang, Chang and Liu, Jiyuan and Hu, Jingtao and Xia, Jingyuan and Yin, Jianping}, 227 | booktitle={Proceedings of the 28th International Joint Conference on Artificial Intelligence}, 228 | pages={3778--3784}, 229 | year={2019}, 230 | organization={AAAI Press} 231 | } 232 | ``` 233 | 234 | --------------------------------------------------------------------------------