Non-Convex Approaches for Low-Rank Tensor Completion under Tubal Sampling
Zheng Tan, Longxiu Huang, HanQin Cai, Yifei Lou
Abstract
Tensor completion is an important problem in modern data analysis. In this work, we investigate a specific sampling strategy, referred to as tubal sampling. We propose two novel non-convex tensor completion frameworks that are easy to implement, named tensor L <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> -L <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> (TL12) and tensor completion via CUR (TCCUR). We test the efficiency of both methods on synthetic data and a color image inpainting problem. Empirical results reveal a trade-off between the accuracy and time efficiency of these two methods in a low sampling ratio. Each of them outperforms some classical completion methods in at least one aspect.
BibTeX
@inproceedings{icassp2023_nonconvexapproac,
title = {Non-Convex Approaches for Low-Rank Tensor Completion under Tubal Sampling},
author = {Zheng Tan and Longxiu Huang and HanQin Cai and Yifei Lou},
booktitle = {ICASSP 2023},
year = {2023}
}