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Journal of Applied MathematicsVolume 2013 2013, Article ID 959403, 6 pages

Research Article

School of Computer Science, Civil Aviation Flight University of China, GuangHan, Sichuan 618307, China

School of Electronic Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan 611731, China

Received 25 September 2012; Revised 2 April 2013; Accepted 30 April 2013

Academic Editor: Xiaojun Wang

Copyright © 2013 Qingshan You et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.

Autor: Qingshan You, Qun Wan, and Haiwen Xu



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