Principal Components as a Small Number of Interpretable Variables: Some Examples

  • I. T. Jolliffe
Part of the Springer Series in Statistics book series (SSS)

Abstract

The original purpose of PCA was to reduce a large number (p) of variables to a much small number (m) of PCs whilst retaining as much as possible of the variation in the p original variables. The technique is especially useful if m « p,and if the m PCs can be readily interpreted.

Keywords

Component Number Crowded Condition Anatomical Measurement Stock Market Price Basic Amenity 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Copyright information

© Springer Science+Business Media New York 1986

Authors and Affiliations

  • I. T. Jolliffe
    • 1
  1. 1.Mathematical InstituteUniversity of KentKentEngland

Personalised recommendations