GAO915311/Lp-TNN

A Robust Low-Rank Matrix Completion Based on Truncated Nuclear Norm and Lp-norm

Lp-TNN

MATLAB code for "A Robust Low-Rank Matrix Completion Based on Truncated Nuclear Norm and Lp-norm".

If you use the code, please cite our paper:

[1] Liang, Hao and Li, Kang and Huang, Jianjun. "A robust low-rank matrix completion based on truncated nuclear norm and Lp-norm", The Journal of Supercomputing, 2022.

Preview

MovieLens-1M Experiment

The MovieLens-1M dataset is available in the following paper:

[2] Harper, F Maxwell and Konstan, Joseph A. "The movielens datasets: History and context", Acm transactions on interactive intelligent systems (tiis), 2015.

As for the comparison methods...

• SVP, SVT, TNNR, and Sp-lp are available in https://github.com/HauLiang/Matrix-Completion-Methods.
• FGSR is available in https://github.com/ImKeTT/Low-rank_Matrix-completion/blob/master/FGSR-for-effecient-low-rank-matrix-recovery/MC_FGSR_PALM.m.

Please check the accompanying license and the license of [2] before using.

@ All rights are reserved by the authors.