A Note on Rank Constrained Solutions to Linear Matrix Equations

Mohan, Shravan

doi:10.2139/ssrn.3245681

Cited by 1 publication

(1 citation statement)

References 4 publications

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“…In recent years, the research area of low-rank tensors has been extended to numerical analysis, computer vision, machine learning, signal and image processing, etc. [27][28][29][30][31]. erefore, it is worthwhile to pay attention and explore how to perform efficient and accurate tensor complementation to recover the information of each dimension of the incomplete tensor.…”

Section: Introductionmentioning

confidence: 99%

Low-Rank Tensor Patching Based on Convolutional Sparse Coding for Communication Data Repair

Hu¹,

Yao

Zhen

2022

Mobile Information Systems

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The study aimed to solve the common problem that hardware limitations and degradation make the data obtained in reality usually incomplete and improve the quality of communication transmission. In this paper, we propose a new low-rank tensor complementation model LRTC-CSC, which is based on tensor kernel parametrization (TNN), preserves the low-rank structure of information while restoring the detail features, and finally solves the problem using the efficient alternating direction multiplier method (ADMM). Based on the low-rank nature of the tensor, adding convolutional sparse coding (CSC) can well represent the characteristics of the high-frequency part of the information to handle the details while recovering the global information. The experimental results show that the training set of this paper saves much time compared with other models in several metrics by using only ten images of similar color for each data. At the same time, the data recovery effect is much higher than the novel TV canonical prior. In particular, the LRTC-CSC model is 5.18 dB higher than the LRTC-TV model in terms of PSNR value for image recovery at a 70% missing rate. The LRTC-CSC model proposed in this paper is more accurate and efficient for communication data restoration.

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