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.
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A survey on multi-view learning Paper
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A study of graph-based system for multi-view clustering Paper code
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Multi-view clustering: A survey Paper
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Multi-view learning overview: Recent progress and new challenges Paper
Papers are listed in the following methods:graph clustering, NMF-based clustering, co-regularized, subspace clustering and multi-kernel clustering
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AAAI15: Large-Scale Multi-View Spectral Clustering via Bipartite Graph Paper code
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IJCAI17: Self-Weighted Multiview Clustering with Multiple Graphs" Paper code
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TKDE2018： One-step multi-view spectral clustering Paper code
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ICDM2019: Consistency Meets Inconsistency: A Unified Graph Learning Framework for Multi-view Clustering Paper code
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TMM 2021: Consensus Graph Learning for Multi-view Clustering code
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NIPS14: Localized Data Fusion for Kernel k-Means Clustering with Application to Cancer Biology Paper code
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IJCAI15： Robust Multiple Kernel K-means using L21-norm Paper code
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AAAI16：Multiple Kernel k-Means Clustering with Matrix-Induced Regularization Paper code
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IJCAI19: Multi-view Clustering with Late Fusion Alignment Maximization Paper code
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TNNLS2019: Multiple kernel clustering with neighbor-kernel subspace segmentation Paper code
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CVPR2015 Diversity-induced Multi-view Subspace Clustering Paper code
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AAAI2018 Consistent and Specific Multi-view Subspace Clustering Paper code
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PR2018: Multi-view Low-rank Sparse Subspace Clustering Paper code
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TIP2019: Split Multiplicative Multi-view Subspace Clustering Paper code
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IJCAI19: Flexible multi-view representation learning for subspace clustering Paper code
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ICCV19: Reciprocal Multi-Layer Subspace Learning for Multi-View Clustering Paper code
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TPAMI 2018: Generalized Latent Multi-View Subspace Clustering(gLMSC)[Paper] [Code]
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STSP 2018: Deep Multimodal Subspace Clustering Networks(DMSC)[Paper] [Code]
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CVPR 2019: AE^2-Nets: Autoencoder in Autoencoder Networks(AE^2-Nets)[Paper] [Code]
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ICML 2019: COMIC: Multi-view Clustering Without Parameter Selection(COMIC)[Paper] [Code]
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IJCAI 2019: Deep Adversarial Multi-view Clustering Network(DAMC)[Paper] [Code]
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IJCAI 2019: Multi-view Spectral Clustering Network(MvSCN)[Paper] [Code]
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TIP 2019: Multi-view Deep Subspace Clustering Networks(MvDSCN)[Paper] [Code]
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AAAI 2020: Cross-modal Subspace Clustering via Deep Canonical Correlation Analysis(CMSC-DCCA)[Paper]
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AAAI 2020: Shared Generative Latent Representation Learning for Multi-View Clustering(DMVCVAE)[Paper] [Code]
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CVPR 2020: End-to-End Adversarial-Attention Network for Multi-Modal Clustering(EAMC)[Paper] [Code]
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IJCAI 2020: Multi-View Attribute Graph Convolution Networks for Clustering(MAGCN)[Paper] [Code]
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IS 2020: Deep Embedded Multi-view Clustering with Collaborative Training(DEMVC)[Paper] [Code]
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TKDE 2020: Joint Deep Multi-View Learning for Image Clustering(DMJC)[Paper]
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WWW 2020: One2Multi Graph Autoencoder for Multi-view Graph Clustering(O2MAC)[Paper] [Code]
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AAAI 2021: Deep Mutual Information Maximin for Cross-Modal Clustering(DMIM)[Paper]
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CVPR 2021: Reconsidering Representation Alignment for Multi-view Clustering(SiMVC&CoMVC)[Paper] [Code]
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DSE 2021: Deep Multiple Auto-Encoder-Based Multi-view Clustering(MVC_MAE)[Paper] [Code]
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ICCV 2021: Multimodal Clustering Networks for Self-supervised Learning from Unlabeled Videos(MCN)[Paper] [Code]
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ICCV 2021: Multi-VAE: Learning Disentangled View-common and View-peculiar Visual Representations for Multi-view Clustering(Multi-VAE)[Paper] [Code]
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IJCAI 2021: Graph Filter-based Multi-view Attributed Graph Clustering(MvAGC)[Paper] [Code]
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Neurcom 2021: Multi-view Subspace Clustering Networks with Local and Global Graph Information(MSCNGL)[Paper] [Code]
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NeurIPS 2021: Multi-view Contrastive Graph Clustering(MCGC)[Paper] [Code]
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TKDE 2021: Self-supervised Discriminative Feature Learning for Deep Multi-view Clustering(SDMVC)[Paper] [Code]
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TKDE 2021: Multi-view Attributed Graph Clustering(MAGC)[Paper] [Code]
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TMM 2021: Deep Multi-view Subspace Clustering with Unified and Discriminative Learning(DMSC-UDL)[Paper] [Code]
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TMM 2021: Self-supervised Graph Convolutional Network for Multi-view Clustering(SGCMC)[Paper] [Code]
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TNNLS 2021: Deep Multiview Collaborative Clustering(DMCC)[Paper]
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TPAMI 2021: Adaptive Graph Auto-Encoder for General Data Clustering(AdaGAE)[Paper] [Code]
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ACMMM 2021: Consistent Multiple Graph Embedding for Multi-View Clustering(CMGEC)[Paper] [Code]
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AAAI 2022: Stationary Diffusion State Neural Estimation for Multiview Clustering(SDSNE)[Paper] [Code]
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CVPR 2022: Deep Safe Multi-View Clustering：Reducing the Risk of Clustering Performance Degradation Caused by View Increase(DSMVC)[Paper] [Code]
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CVPR 2022: Multi-level Feature Learning for Contrastive Multi-view Clustering(MFLVC)[Paper] [Code]
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IJCAI 2022: Contrastive Multi-view Hyperbolic Hierarchical Clustering(CMHHC)[Paper]
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NN 2022: Multi-view Graph Embedding Clustering Network：Joint Self-supervision and Block Diagonal Representation(MVGC)[Paper] [Code]
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IPM 2023: Joint Contrastive Triple-learning for Deep Multi-view Clustering(JCT)[Paper] [Code]
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2023: Tensorized Adaptive Deep Multi-view Subspace Clustering[Code]
- NeurIPS 2019: CPM-Nets: Cross Partial Multi-View Networks[Paper] [Code]
- TIP 2020: Generative Partial Multi-View Clustering[Paper] [Code]
- CVPR 2021: COMPLETER: Incomplete Multi-view Clustering via Contrastive Prediction[Paper] [Code]
- TIP 2021: iCmSC: Incomplete Cross-modal Subspace Clustering[Paper] [Code]
- TPAMI 2022: Deep Partial Multi-View Learning[Paper] [Code]
- TPAMI 2022: Dual Contrastive Prediction for Incomplete Multi-view Representation Learning[Paper] [Code]
- ICML 2022: Deep Safe Incomplete Multi-view Clustering: Theorem and Algorithm[Paper] [Code]
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TPAMI 2021: Multi-view Clustering: A Scalable and Parameter-free Bipartite Graph Fusion Method Paper code fvnh
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AAAI20: Large-scale Multi-view Subspace Clustering in Linear Time paper code
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ACM MM2021: Scalable Multi-view Subspace Clustering with Unified Anchors paper code
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TIP22: Fast Parameter-Free Multi-View Subspace Clustering with Consensus Anchor Guidance paper code
- Applied Soft Computing 2021: An Evolutionary Many-objective Approach to Multiview Clustering Using Feature and Relational Data Paper code
- 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
- 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}
}