Paper: "Deep Double Incomplete Multi-view Multi-label Learning with Incomplete Labels and Missing Views", accepted by TNNLS.
you can run "python DIMC-semi.py" to train semi-supervised model and get the best results! or you can run "python DIMC-sup.py" to train supervised model and get the best results!
corel5k dataset is provided for a demo!
@article{wen2023deep, title={Deep Double Incomplete Multi-View Multi-Label Learning With Incomplete Labels and Missing Views}, author={Wen, Jie and Liu, Chengliang and Deng, Shijie and Liu, Yicheng and Fei, Lunke and Yan, Ke and Xu, Yong}, journal={IEEE Transactions on Neural Networks and Learning Systems}, year={2023}, publisher={IEEE} }