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Principal Component Analysis

  • Gopinath Rebala
  • Ajay Ravi
  • Sanjay Churiwala
Chapter

Abstract

Principal component analysis (PCA) is a statistical process that allows reducing number of variables from a given dataset to a smaller set of variables that can be used in data analysis. The reduced set of variables retain the variance present in the original dataset. This is very useful in machine learning where the amount of data required for training is related to number of variables used in modeling.

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Gopinath Rebala
    • 1
  • Ajay Ravi
    • 2
  • Sanjay Churiwala
    • 3
  1. 1.OpsMx IncSan RamonUSA
  2. 2.San JoseUSA
  3. 3.HyderabadIndia

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