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Abstract

The central idea of principal component analysis (PCA) is to reduce the dimensionality of a data set which consists of a large number of interrelated variables, while retaining as much as possible of the variation present in the data set. This is achieved by transforming to a new set of variables, the principal components (PCs), which are uncorrelated, and which are ordered so that the first few retain most of the variation present in all of the original variables.

Keywords

Principal Component Analysis Lagrange Multiplier Original Variable Large Eigenvalue Multivariate Normal Distribution 
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 Science+Business Media New York 1986

Authors and Affiliations

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

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