Awesome-Continual-Multi-view-clustering

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Awesome-Continual-Multi-view-clustering

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

Awesome-Continual-Multi-view-clustering 최신버젼 다운로드

최종 버전 다운로드 (.zip)

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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.

## 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}
}

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