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Principal Component Analysis

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

Abstract

Principal component analysis (PCA) is the problem of fitting a low-dimensional affine subspace to a set of data points in a high-dimensional space. PCA is, by now, well established in the literature, and has become one of the most useful tools for data modeling, compression, and visualization.

Keywords

Asymptotic Mean Squared Error (AMSE) Probabilistic Principal Component Analysis (PPCA) PPCA Model Singular Value Thresholding Hard Thresholding 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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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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