Non-convex approaches for low-rank tensor completion under tubal sampling
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Abstract (arXiv)
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 L1-L2 (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.
Key facts. Cited by 1 paper in the map (data through 13 Oct 2026). Builds on 9 of its 26 references that are in the map. First posted on arXiv 17 Mar 2023.
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9 of 26 references.
References · 9
- 2018Tensor Robust Principal Component Analysis with A New Tensor Nuclear Normcited by 111
- 2015Exact tensor completion using t-SVDcited by 70
- 2014Novel methods for multilinear data completion and de-noising based on tensor-SVDcited by 56
- 2018Tensor Ring Decomposition with Rank Minimization on Latent Space: An Efficient Approach for Tensor Completioncited by 42
- 2017Multi-dimensional imaging data recovery via minimizing the partial sum of tubal nuclear normcited by 19
- 2016Low-tubal-rank Tensor Completion using Alternating Minimizationcited by 18
- 2016Fast L1-L2 minimization via a proximal operatorcited by 16
- 2021Mode-wise Tensor Decompositions: Multi-dimensional Generalizations of CUR Decompositionscited by 13
- 2022Matrix Completion with Cross-Concentrated Sampling: Bridging Uniform Sampling and CUR Samplingcited by 12
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