hyzhang98 / NCARL

Implementation of "Matrix Completion via Non-Convex Relaxation and Adaptive Correlation Learning", IEEE Transactions on Pattern Analysis and Machine Intelligence.

Matrix Completion via Non-Convex Relaxation and Adaptive Correlation Learning

This repository is our implementation of

Xuelong Li, Hongyuan Zhang, and Rui Zhang, "Matrix Completion via Non-Convex Relaxation and Adaptive Correlation Learning," IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022.

In this paper, we introduce a new surrogate for matrix completion, which is equivalent to the nuclear norm.

In particular, we prove the upper-bound of an approximate/inexact closed-form solution, which is a crucial step of the optimization. The surrogate and its optimization make the matrix completion more compatible for additional learning mechanisms.

If you have issues, please email:

[e-mail] or [e-mail].

Descriptions of Files

• image_main.m: An example of how to run the code
• ncarl_no_noise: The source code of NCARL
• ncarl.m: The noisy extension

Citation

@article{NCARL,
  author={Li, Xuelong and Zhang, Hongyuan and Zhang, Rui},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, 
  title={Matrix Completion via Non-Convex Relaxation and Adaptive Correlation Learning}, 
  year={2022},
  volume={},
  number={},
  pages={1-1},
  doi={10.1109/TPAMI.2022.3157083}
}