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arXiv · 2303.12721

Non-convex approaches for low-rank tensor completion under tubal sampling

Zheng Tan·Longxiu Huang·HanQin Cai·Yifei Lou
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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 $L_1$-$L_2$ (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.

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Zheng Tan, Longxiu Huang, HanQin Cai, Yifei Lou. 2023-03-17. Non-convex approaches for low-rank tensor completion under tubal sampling. https://doi.org/10.1109/icassp49357.2023.10094847

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