Journal of Applied Mathematics

  • J. Appl. Math.
  • Volume 2013, Special Issue (2013), Article ID 959403, 6 pages.

Restricted Isometry Property of Principal Component Pursuit with Reduced Linear Measurements

Qingshan You, Qun Wan, and Haiwen Xu

Full-text: Open access

Abstract

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.

Article information

Source
J. Appl. Math., Volume 2013, Special Issue (2013), Article ID 959403, 6 pages.

Dates
First available in Project Euclid: 14 March 2014

Permanent link to this document
https://projecteuclid.org/euclid.jam/1394807802

Digital Object Identifier
doi:10.1155/2013/959403

Zentralblatt MATH identifier
1268.90050

Citation

You, Qingshan; Wan, Qun; Xu, Haiwen. Restricted Isometry Property of Principal Component Pursuit with Reduced Linear Measurements. J. Appl. Math. 2013, Special Issue (2013), Article ID 959403, 6 pages. doi:10.1155/2013/959403. https://projecteuclid.org/euclid.jam/1394807802


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