This paper proposes an imbalanced low-rank tensor completion method using latent tensor ring components and proximal alternating minimization, achieving better results with less computational cost.
src/trfold.m: Implements the tensor ring folding method.src/trunfold.m: Implements the tensor ring unfolding method.src/TRLMF_PAM.m: Implements the imbalanced low-rank tensor completion method based on the proximal alternating minimization algorithm.
An example is provided in the test_TRLMF_color_image.m file, demonstrating how to use the above functions for tensor completion. Running this file will show a comparison of the original image, the observed image, and the recovered image.
If you use this code in your research, please cite the following paper:
@article{qiu2022imbalanced,
title={Imbalanced low-rank tensor completion via latent matrix factorization},
author={Qiu, Yuning and Zhou, Guoxu and Zeng, Junhua and Zhao, Qibin and Xie, Shengli},
journal={Neural Networks},
volume={155},
pages={369--382},
year={2022},
publisher={Elsevier}
}