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.
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 | |
|---|---|---|---|---|
| Incremental Multi-View Clustering: Exploring Stream-View Correlations to Learn Consistency and Diversity (CDIMVC) | 2025 | TKDE | link | matlab |
| Multi-View Clustering With Incremental Instances and Views (MVC-IIV) | 2025 | TIP | link | matlab |
| Anchor-guided Sample-and-feature Incremental Alignment Framework for Multi-view Clustering (ASIA-MVC) | 2025 | TCSVT | link | matlab |
| Incremental Nyström-based Multiple Kernel Clustering (INMKC) | 2025 | AAAI | link | matlab |
| Incremental Multiview Clustering With Continual Information Bottleneck Method | 2025 | IEEE T SYST MAN CY-S | link | - |
| AdaptCMVC: Robust Adaption to Incremental Views in Continual Multi-view Clustering (AdaptCMVC) | 2025 | CVPR | link | python |
| Multi-View Incremental Learning with Structured Hebbian Plasticity for Enhanced Fusion Efficiency (MVIL) | 2025 | AAAI | link | - |
| Incremental Multi-view Clustering using Barycentric Coordinate Representation (IMBC) | 2024 | IJCNN | link | - |
| One-step incremental multi-view spectral clustering based on graph linkage learning (OIMvSC) | 2024 | Neurocomputing | 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 |
https://github.com/wanxinhang/Awesome-Semi-supervised-Multi-view-classification/
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.
@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} }
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