Robust Principal Component Analysis

  • René Vidal
  • Yi Ma
  • S. Shankar Sastry
Part of the Interdisciplinary Applied Mathematics book series (IAM, volume 40)


In the previous chapter, we considered the PCA problem under the assumption that all the sample points are drawn from the same statistical or geometric model: a low-dimensional subspace.


Missing Entries Matrix Completion Principal Component Pursuit (PCP) Alternating Direction Method Of Multipliers (ADMM) Robust PCA (RPCA) 
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Copyright information

© Springer-Verlag New York 2016

Authors and Affiliations

  • René Vidal
    • 1
  • Yi Ma
    • 2
  • S. Shankar Sastry
    • 3
  1. 1.Center for Imaging Science Department of Biomedical EngineeringJohns Hopkins UniversityBaltimoreUSA
  2. 2.School of Information Science and Technology ShanghaiTech UniversityShanghaiChina
  3. 3.Department of Electrical Engineering and Computer ScienceUniversity of California BerkeleyBerkeleyUSA

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