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J. Patt. Recogn. Artif. Intell."],"published-print":{"date-parts":[[2023,3,30]]},"abstract":"<jats:p> 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 [Formula: see text] norm minimization is proposed in this paper, where the [Formula: see text] norm is the summation of the [Formula: see text]-power [Formula: see text] of [Formula: see text] 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 [Formula: see text] 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. <\/jats:p>","DOI":"10.1142\/s0218001423500076","type":"journal-article","created":{"date-parts":[[2023,1,31]],"date-time":"2023-01-31T02:09:11Z","timestamp":1675130951000},"source":"Crossref","is-referenced-by-count":1,"title":["An Improved Low-Rank Matrix Fitting Method Based on Weighted L1,p Norm Minimization for Matrix Completion"],"prefix":"10.1142","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6397-0889","authenticated-orcid":false,"given":"Qing","family":"Liu","sequence":"first","affiliation":[{"name":"School of Electronic and Information Engineering, West Anhui University, Lu\u2019an, Anhui, P. R. 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