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Low-rank tensor recovery via non-convex regularization, structured factorization and spatio-temporal characteristics

Home Page: https://doi.org/10.1016/j.patcog.2023.109343

MATLAB 100.00%
low-rank-approximation spatio-temporal-characteristics tensor-completion dynamic-background tensor-robust-principle-component-analysis

lprn's Introduction

Low-rank tensor recovery

This is MATLAB implementation of paper "Low-rank tensor recovery via non-convex regularization, structured factorization and spatio-temporal characteristics".

If you have questions about the code, please email: [email protected]

Academic Homepage: https://quanyu.netlify.app/

Citation

If these codes and dataset are helpful for you, please cite this paper:

@article{YU2023109343,
title = {Low-rank tensor recovery via non-convex regularization, structured factorization and spatio-temporal characteristics},
journal = {Pattern Recognition},
volume = {137},
pages = {109343},
year = {2023},
issn = {0031-3203},
doi = {https://doi.org/10.1016/j.patcog.2023.109343},
url = {https://www.sciencedirect.com/science/article/pii/S0031320323000444},
author = {Quan Yu and Ming Yang}
}

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