The caparison of DSIMVC and DSIMVC++ framework for incomplete multi-view clustering. In DSIMVC++, the proposed GCFAgg module is integrated to obtain the consensus representation and consensus clustering assignment, and the clustering loss is enhanced by the proposed SgCL module.

pytorch==1.12.1

numpy>=1.21.6

scikit-learn>=1.0.2

The datasets are placed in "data" folder. The others dataset could be downloaded from cloud. key: data

• Before run, please carefully read ''Obtain-S.docx'', and refer to the steps inside it to modify the code in order to obtain S.

Work&Code is inspired by DSIMVC

If you find our work useful in your research, please consider citing:

@InProceedings{Yan_2023_CVPR,
    author    = {Yan, Weiqing and Zhang, Yuanyang and Lv, Chenlei and Tang, Chang and Yue, Guanghui and Liao, Liang and Lin, Weisi},
    title     = {GCFAgg: Global and Cross-View Feature Aggregation for Multi-View Clustering},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2023},
    pages     = {19863-19872}
}

If you have any problems, contact me via zhangyuanyang922@gmail.com.