PyTorch implementation for Robust Multi-view Clustering with Incomplete Information (TPAMI 2022).
pytorch>=1.5.0
numpy>=1.18.2
scikit-learn>=0.22.2
munkres>=1.1.2
logging>=0.5.1.2
The Scene-15 and Reuters-dim10 datasets are placed in "datasets" folder. Another datasets could be downloaded from Google cloud or Baidu cloud with password "rqv4".
Train a model with different settings
# Partially Aligned, Table 1, Table 6
python run.py --data 0 --gpu 0 --settings 0 --aligned-prop 0.5 --complete-prop 1.0
# Fully Aligned, Table 1, Table 6
python run.py --data 0 --gpu 0 --settings 0 --aligned-prop 1.0 --complete-prop 1.0
# Incomplete, Table 5, Table 6
python run.py --data 0 --gpu 0 --settings 1 --aligned-prop 1.0 --complete-prop 0.5
# Complete, Table 5, Table 6
python run.py --data 0 --gpu 0 --settings 1 --aligned-prop 1.0 --complete-prop 1.0
# PVP + PSP, Figure 9, Table 6
python run.py --data 0 --gpu 0 --settings 2 --aligned-prop 0.5 --complete-prop 0.5-
--data: choice of datasets. -
--gpu: which gpu to run. -
--settings: 0-PVP, 1-PSP, 2-Both. -
--aligned-prop: known aligned proportions for training. -
--complete-prop: known complete proportions for training.Parameters: More parameters and descriptions can be found in the script.
Training Log: The training log will be saved in
log/
If you find our work useful in your research, please consider citing:
@inproceedings{yang2021MvCLN,
title={Partially View-aligned Representation Learning with Noise-robust Contrastive Loss},
author={Mouxing Yang, Yunfan Li, Zhenyu Huang, Zitao Liu, Peng Hu, Xi Peng},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month={June},
year={2021}
}
@article{yang2022SURE,
title={Robust Multi-view Clustering with Incomplete Information},
author={Yang, Mouxing and Li, Yunfan and Hu, Peng and Bai, Jinfeng and Lv, Jian Cheng and Peng, Xi},
journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
year={2022},
}