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1 results

  • 15 - Canonical Correlation Analysis

  • Timothy DelSole, George Mason University, Virginia, Michael Tippett, Columbia University, New York
  • Book:
    Statistical Methods for Climate Scientists
    Published online:
    03 February 2022
    Print publication:
    24 February 2022, pp 335-365

  • Summary

    The correlation coefficient measures the linear relation between scalar X and scalar Y. How can the linear relation between vector X and vector Y be measured?Canonical Correlation Analysis (CCA) provides a way. CCA finds a linear combination of X, and a (separate) linear combination of Y, that maximizes the correlation. The resulting maximized correlation is called a canonical correlation. More generally, CCA decomposes two sets of variables into an ordered sequence of component pairs ordered such that the first pair has maximum correlation, the second has maximum correlation subject to being uncorrelated with the first, and so on. The entire decomposition can be derived from a Singular Value Decomposition of a suitable matrix. If the dimension of the X and Y vectors is too large, overfitting becomes a problem. In this case, CCA often is computed using a few principal components of X and Y. The criterion for selecting the number of principal components is not standard. The Mutual Information Criterion (MIC) introduced in Chapter 14 is used in this chapter.