Chapter 6. Principal Component Analysis and Whitening

  1. Aapo Hyvärinen,
  2. Juha Karhunen and
  3. Erkki Oja

Published Online: 15 MAY 2002

DOI: 10.1002/0471221317.ch6

Independent Component Analysis

Independent Component Analysis

How to Cite

Hyvärinen, A., Karhunen, J. and Oja, E. (2001) Principal Component Analysis and Whitening, in Independent Component Analysis, John Wiley & Sons, Inc., New York, USA. doi: 10.1002/0471221317.ch6

Author Information

  1. Neural Networks Research Center, Helsinki University of Technology, Finland

Publication History

  1. Published Online: 15 MAY 2002
  2. Published Print: 21 MAY 2001

ISBN Information

Print ISBN: 9780471405405

Online ISBN: 9780471221319



  • principal component analysis (PCA);
  • whitening;
  • on-line learning;
  • factor analysis;
  • orthogonalization


The basic principal component analysis (PCA) problem is outlined in this chapter. Both the closed-form solution and on-line learning algorithms for PCA are reviewed. Next, the related linear statistical technique of factor analysis is discussed. The chapter is concluded by presenting how data can be preprocessed by whitening, removing the effect of first-and second-order statistics, which is very helpful as the first step in ICA.