A novel robust principal component analysis algorithm of nonconvex rank approximation
type: Item
instance of: scholarly article
title: A novel robust principal component analysis algorithm of nonconvex rank approximation
zbMATH Open document ID: 1459.94027
DOI: 10.1155/2020/9356935
author: Yanyan Li
published in: Mathematical Problems in Engineering
publication date: 2020-10-14T00:00:00Z
review text: Summary: Noise exhibits low rank or no sparsity in the low-rank matrix recovery, and the nuclear norm is not an accurate rank approximation of low-rank matrix. In the present study, to solve the mentioned problem, a novel nonconvex approximation function of the low-rank matrix was proposed. Subsequently, based on the nonconvex rank approximation function, a novel model of robust principal component analysis was proposed. Such model was solved with the alternating direction method, and its convergence was verified theoretically. Subsequently, the background separation experiments were performed on the Wallflower and SBMnet datasets. Furthermore, the effectiveness of the novel model was verified by numerical experiments.
Mathematics Subject Classification ID: 94A08
Mathematics Subject Classification ID: 90C25
zbMATH DE Number: 7260988
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2020/9356935
OpenAlex ID: W3089517991
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description: scientific article; zbMATH DE number 7260988