A Novel Robust Principal Component Analysis Algorithm of Nonconvex Rank Approximation
Joint Authors
Source
Mathematical Problems in Engineering
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-17, 17 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-09-30
Country of Publication
Egypt
No. of Pages
17
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Zhu, E.& Xu, M.& Pi, D.. 2020. A Novel Robust Principal Component Analysis Algorithm of Nonconvex Rank Approximation. Mathematical Problems in Engineering،Vol. 2020, no. 2020, pp.1-17.
https://search.emarefa.net/detail/BIM-1202172
Modern Language Association (MLA)
Zhu, E.…[et al.]. A Novel Robust Principal Component Analysis Algorithm of Nonconvex Rank Approximation. Mathematical Problems in Engineering No. 2020 (2020), pp.1-17.
https://search.emarefa.net/detail/BIM-1202172
American Medical Association (AMA)
Zhu, E.& Xu, M.& Pi, D.. A Novel Robust Principal Component Analysis Algorithm of Nonconvex Rank Approximation. Mathematical Problems in Engineering. 2020. Vol. 2020, no. 2020, pp.1-17.
https://search.emarefa.net/detail/BIM-1202172
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1202172