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].

• 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

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

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

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

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

1. CVPR2019: AE^2-Nets: Autoencoder in Autoencoder Networks Paper code

2. TIP2019: Multi-view Deep Subspace Clustering Networks Paper code

3. TKDE2020: Joint Deep Multi-View Learning for Image Clustering Paper

4. ICML2019: COMIC: Multi-view Clustering Without Parameter Selection paper code

5. IJCAI2019: Multi-view Spectral Clustering Network paper code

6. IJCAI2019: Deep Adversarial Multi-view Clustering Network paper code

7. KBS2021：Multi-view clustering via deep concept factorization code

1. TPAMI2019: Binary Multi-View Clustering Paper code

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

1. TNNLS2019: Marginalized Multiview Ensemble Clustering Paper code

1. TPAMI 2021: Multi-view Clustering: A Scalable and Parameter-free Bipartite Graph Fusion Method Paper code

2. AAAI20: arge-scale Multi-view Subspace Clustering in Linear Time paper code

3. ACM MM2021: Scalable Multi-view Subspace Clustering with Unified Anchors paper code

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

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

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}
}