An Improved Low-Rank Matrix Fitting Method Based on Weighted L1,p Norm Minimization for Matrix Completion

Qing Liu; Qing Jiang; Jing ZhangBin JiangZhengyu Liu

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An Improved Low-Rank Matrix Fitting Method Based on Weighted L1,p Norm Minimization for Matrix Completion

机译：An Improved Low-Rank Matrix Fitting Method Based on Weighted L1,p Norm Minimization for Matrix Completion

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摘要

Low-rank matrix completion, which aims to recover a matrix with many missing values, has attracted much attention in many fields of computer science. A low-rank matrix fitting (LMaFit) method has been proposed for fast matrix completion recently. However, this method cannot converge accurately on matrices of real-world images. For improving the accuracy of LMaFit method, an improved low-rank matrix fitting (ILMF) method based on the weighted L1,p norm minimization is proposed in this paper, where the L1,p norm is the summation of the p-power (0

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《International journal of pattern recognition and artificial intelligence》 |2023年第4期|2350007.1-2350007.16|共16页
作者
Qing Liu; Qing Jiang; Jing ZhangBin JiangZhengyu Liu;
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作者单位

School of Electronic and Information Engineering, West Anhui University, Lu’an, Anhui,P. R. China||School of Mathematics and Big Data, Anhui University of Science and Technology Huainan, Anhui,P. R. China;

School of Electronic and Information Engineering, West Anhui University, Lu’an, Anhui,P. R. China;

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收录信息美国《科学引文索引》(SCI);美国《工程索引》(EI);
原文格式 PDF
正文语种英语
中图分类
关键词
Low rank; L1; p norm; matrix completion; QR decomposition;

An Improved Low-Rank Matrix Fitting Method Based on Weighted L1,p Norm Minimization for Matrix Completion

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