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Robust PCA Using Nonconvex Rank Approximation and Sparse Regularizer

Dong, Jing, Xue, Zhichao and Wang, Wenwu (2019) Robust PCA Using Nonconvex Rank Approximation and Sparse Regularizer Circuits, Systems and Signal Processing, 39. pp. 3086-3104.

DongW_CSSP_2020_preprint.pdf - Accepted version Manuscript

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We consider the robust principal component analysis (RPCA) problem where the observed data is decomposed to a low-rank component and a sparse component. Conventionally, the matrix rank in RPCA is often approximated using a nuclear norm. Recently, RPCA has been formulated using the nonconvex ` -norm, which provides a closer approximation to the matrix rank than the traditional nuclear norm. However, the low-rank component generally has sparse property, especially in the transform domain. In this paper, a sparsity-based regularization term modeled with `1-norm is introduced to the formulation. An iterative optimization algorithm is developed to solve the obtained optimization problem. Experiments using synthetic and real data are utilized to validate the performance of the proposed method.

Item Type: Article
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering
Authors :
Dong, Jing
Xue, Zhichao
Date : 21 November 2019
Funders : National Natural Science Foundation of China, Natural Science Foundation of Jiangsu Province of China, Natural Science Foundation of the Higher Education Institutions of Jiangsu Province of China
DOI : 10.1007/s00034-019-01310-y
Grant Title : National Natural Science Foundation of China Grant
Copyright Disclaimer : Copyright © 2019, Springer Nature
Depositing User : James Marshall
Date Deposited : 04 Jun 2020 10:49
Last Modified : 12 Nov 2020 02:08

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