Collections for Awesome-Continual-Multi-view-clustering methods (papers and codes). We are looking forward for other participants to share their papers and codes. If interested, please contact [email protected].
Update at May 2024.
Existing multi-view overlooks scenarios where data views are collected sequentially, i.e., real-time data. Due to privacy issues or memory burden, previous views are not available with time in these situations. Continual Multi-view clustering aims to conduct the clustering task in this setting.
| Paper | Year | Publish | Code | |
|---|---|---|---|---|
| Live and Learn: Continual Action Clustering with Incremental Views (CAC) | 2024 | AAAI | link | - |
| Contrastive Continual Multi-view Clustering with Filtered Structural Fusion (CCMVC-FSF) | 2024 | arVix | link | - |
| Fast Continual Multi-View Clustering With Incomplete Views (FCMVC-IV) | 2024 | TIP | link | matlab |
| Continual Multi-view Clustering (CMVC) | 2022 | ACM MM | link | matlab |
| Incremental multi-view spectral clustering with sparse and connected graph learning (SCGL) | 2021 | NN | link | matlab |
| Incremental multi-view spectral clustering (IMSC) | 2019 | KBS | link | matlab |
https://github.com/wanxinhang/Awesome-Semi-supervised-Multi-view-classification/
https://github.com/wangsiwei2010/awesome-multi-view-clustering
https://github.com/liangnaiyao/multiview_learning
@ARTICLE{10506102, author={Wan, Xinhang and Xiao, Bin and Liu, Xinwang and Liu, Jiyuan and Liang, Weixuan and Zhu, En}, journal={IEEE Transactions on Image Processing}, title={Fast Continual Multi-View Clustering With Incomplete Views}, year={2024}, volume={33}, number={}, pages={2995-3008}, keywords={Complexity theory;Kernel;Task analysis;Clustering algorithms;Real-time systems;Privacy;Fuses;Multi-view learning;clustering;continual learning}, doi={10.1109/TIP.2024.3388974}}
@inproceedings{10.1145/3503161.3547864, author = {Wan, Xinhang and Liu, Jiyuan and Liang, Weixuan and Liu, Xinwang and Wen, Yi and Zhu, En}, title = {Continual Multi-View Clustering}, year = {2022}, isbn = {9781450392037}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3503161.3547864}, doi = {10.1145/3503161.3547864}, booktitle = {Proceedings of the 30th ACM International Conference on Multimedia}, pages = {3676–3684}, numpages = {9}, keywords = {multi-view clustering, consensus partition matrix, late fusion, continual learning}, location = {Lisboa, Portugal}, series = {MM '22} }
@misc{wan2024contrastive, title={Contrastive Continual Multi-view Clustering with Filtered Structural Fusion}, author={Xinhang Wan and Jiyuan Liu and Hao Yu and Ao Li and Xinwang Liu and Ke Liang and Zhibin Dong and En Zhu}, year={2024}, eprint={2309.15135}, archivePrefix={arXiv}, primaryClass={cs.LG} }
@article{Yan_Gan_Mao_Ye_Yu_2024, title={Live and Learn: Continual Action Clustering with Incremental Views}, volume={38}, url={https://ojs.aaai.org/index.php/AAAI/article/view/29561}, DOI={10.1609/aaai.v38i15.29561}, number={15}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Yan, Xiaoqiang and Gan, Yingtao and Mao, Yiqiao and Ye, Yangdong and Yu, Hui}, year={2024}, month={Mar.}, pages={16264-16271} }
@article{YIN2021260, title = {Incremental multi-view spectral clustering with sparse and connected graph learning}, journal = {Neural Networks}, volume = {144}, pages = {260-270}, year = {2021}, issn = {0893-6080}, doi = {https://doi-org-s.libyc.nudt.edu.cn:443/10.1016/j.neunet.2021.08.031}, url = {https://www-sciencedirect-com-s.libyc.nudt.edu.cn:443/science/article/pii/S0893608021003440}, author = {Hongwei Yin and Wenjun Hu and Zhao Zhang and Jungang Lou and Minmin Miao}, keywords = {Multi-view clustering, Incremental clustering, Sparse graph learning, Connected graph learning, Spectral embedding}, }
@article{ZHOU201973, title = {Incremental multi-view spectral clustering}, journal = {Knowledge-Based Systems}, volume = {174}, pages = {73-86}, year = {2019}, issn = {0950-7051}, doi = {https://doi-org-s.libyc.nudt.edu.cn:443/10.1016/j.knosys.2019.02.036}, url = {https://www-sciencedirect-com-s.libyc.nudt.edu.cn:443/science/article/pii/S0950705119301030}, author = {Peng Zhou and Yi-Dong Shen and Liang Du and Fan Ye and Xuejun Li}, keywords = {Multi-view clustering, Spectral clustering, Incremental learning}, }
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