Restricted Isometry Property of Principal Component Pursuit with Reduced Linear Measurements
Joint Authors
Wan, Qun
Xu, Haiwen
You, Qingshan
Source
Journal of Applied Mathematics
Issue
Vol. 2013, Issue 2013 (31 Dec. 2013), pp.1-6, 6 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2013-05-23
Country of Publication
Egypt
No. of Pages
6
Main Subjects
Abstract EN
The principal component prsuit with reduced linear measurements (PCP_RLM) has gained great attention in applications, such as machine learning, video, and aligning multiple images.
The recent research shows that strongly convex optimization for compressive principal component pursuit can guarantee the exact low-rank matrix recovery and sparse matrix recovery as well.
In this paper, we prove that the operator of PCP_RLM satisfies restricted isometry property (RIP) with high probability.
In addition, we derive the bound of parameters depending only on observed quantities based on RIP property, which will guide us how to choose suitable parameters in strongly convex programming.
American Psychological Association (APA)
You, Qingshan& Wan, Qun& Xu, Haiwen. 2013. Restricted Isometry Property of Principal Component Pursuit with Reduced Linear Measurements. Journal of Applied Mathematics،Vol. 2013, no. 2013, pp.1-6.
https://search.emarefa.net/detail/BIM-511518
Modern Language Association (MLA)
You, Qingshan…[et al.]. Restricted Isometry Property of Principal Component Pursuit with Reduced Linear Measurements. Journal of Applied Mathematics No. 2013 (2013), pp.1-6.
https://search.emarefa.net/detail/BIM-511518
American Medical Association (AMA)
You, Qingshan& Wan, Qun& Xu, Haiwen. Restricted Isometry Property of Principal Component Pursuit with Reduced Linear Measurements. Journal of Applied Mathematics. 2013. Vol. 2013, no. 2013, pp.1-6.
https://search.emarefa.net/detail/BIM-511518
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-511518