Robust Tensor Completion Using Transformed Tensor SVD
Machine Learning
2019-07-03 v1 Computer Vision and Pattern Recognition
Machine Learning
Abstract
In this paper, we study robust tensor completion by using transformed tensor singular value decomposition (SVD), which employs unitary transform matrices instead of discrete Fourier transform matrix that is used in the traditional tensor SVD. The main motivation is that a lower tubal rank tensor can be obtained by using other unitary transform matrices than that by using discrete Fourier transform matrix. This would be more effective for robust tensor completion. Experimental results for hyperspectral, video and face datasets have shown that the recovery performance for the robust tensor completion problem by using transformed tensor SVD is better in PSNR than that by using Fourier transform and other robust tensor completion methods.
Keywords
Cite
@article{arxiv.1907.01113,
title = {Robust Tensor Completion Using Transformed Tensor SVD},
author = {Guangjing Song and Michael K. Ng and Xiongjun Zhang},
journal= {arXiv preprint arXiv:1907.01113},
year = {2019}
}