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Awesome Continual Multi-view Clustering is a collection of SOTA, novel continual multi-view clustering methods (papers, codes).

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Awesome-Continual-Multi-view-clustering

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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 wanxinhang@nudt.edu.cn.

Update at May 2024.

What's Continual Multi-view clustering?

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.

Papers

Paper Year Publish PDF Code
Multi-View Incremental Learning with Structured Hebbian Plasticity for Enhanced Fusion Efficiency (MVIL) 2025 AAAI link -
Contrastive Continual Multi-view Clustering with Filtered Structural Fusion (CCMVC-FSF) 2024 TNNLS link matlab
A Lightweight Anchor-Based Incremental Framework to Multi-view Clustering (LAIMVC) 2024 ACM MM link matlab
Live and Learn: Continual Action Clustering with Incremental Views (CAC) 2024 AAAI 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

Some-useful-links:

https://github.com/wanxinhang/Awesome-Semi-supervised-Multi-view-classification/

https://github.com/dugzzuli/A-Survey-of-Multi-view-Clustering-Approaches#the-information-fusion-strategy

https://github.com/wangsiwei2010/awesome-multi-view-clustering

https://github.com/liangnaiyao/multiview_learning

https://github.com/Jeaninezpp/Incomplete-multi-view-clustering#incomplete-multi-view-clustering.

Citations:

@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}}

@ARTICLE{10777843, author={Wan, Xinhang and Liu, Jiyuan and Yu, Hao and Qu, Qian and Li, Ao and Liu, Xinwang and Liang, Ke and Dong, Zhibin and Zhu, En}, journal={IEEE Transactions on Neural Networks and Learning Systems}, title={Contrastive Continual Multiview Clustering With Filtered Structural Fusion}, year={2024}, volume={}, number={}, pages={1-14}, keywords={Kernel;Information filters;Privacy;Correlation;Contrastive learning;Clustering methods;Technological innovation;Stability plasticity;Real-time systems;Complexity theory;Clustering;continual learning;multiview learning}, doi={10.1109/TNNLS.2024.3502455}}

@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} }

@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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