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An Improved Low-Rank Matrix Fitting Method Based on Weighted L1,p Norm Minimization for Matrix Completion.
- Source :
- International Journal of Pattern Recognition & Artificial Intelligence; Mar2023, Vol. 37 Issue 4, p1-16, 16p
- Publication Year :
- 2023
-
Abstract
- 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 L 1 , p norm minimization is proposed in this paper, where the L 1 , p norm is the summation of the p -power (0 < p < 2) of L 1 norms of rows in a matrix. In the proposed method, i.e. the ILMF method, the incomplete matrix that may be corrupted by noises is decomposed into the summation of a low-rank matrix and a noise matrix at first. Then, a weighted L 1 , p norm minimization problem is solved by using an alternating direction method for improving the accuracy of matrix completion. Experimental results on real-world images show that the ILMF method has much better performances in terms of both the convergence accuracy and convergence speed than the compared methods. [ABSTRACT FROM AUTHOR]
- Subjects :
- LOW-rank matrices
MATRIX norms
COMPUTER science
PROBLEM solving
Subjects
Details
- Language :
- English
- ISSN :
- 02180014
- Volume :
- 37
- Issue :
- 4
- Database :
- Complementary Index
- Journal :
- International Journal of Pattern Recognition & Artificial Intelligence
- Publication Type :
- Academic Journal
- Accession number :
- 163018835
- Full Text :
- https://doi.org/10.1142/S0218001423500076