pcaApply

 Companion function to pca.

 Use pca.m to retrieve the principal components U and the mean mu from a
 set of vectors x, then use pcaApply to get the first k coefficients of
 x in the space spanned by the columns of U. See pca for general usage.

 If x is large, pcaApply first splits and processes x in parts. This
 allows pcaApply to work even for very large arrays.

 This may prove useful:
  siz=size(X);  k=100;  Uim=reshape(U(:,1:k),[siz(1:end-1) k ]);

 USAGE
  [ Yk, Xhat, avsq ] = pcaApply( X, U, mu, k )

 INPUTS
  X           - data for which to get PCA coefficients
  U           - returned by pca.m
  mu          - returned by pca.m
  k           - number of principal coordinates to approximate X with

 OUTPUTS
  Yk          - first k coordinates of X in column space of U
  Xhat        - approximation of X corresponding to Yk
  avsq        - measure of squared error normalized to fall between [0,1]

 EXAMPLE

 See also PCA, PCAVISUALIZE

 Piotr's Computer Vision Matlab Toolbox      Version 2.0
 Copyright 2014 Piotr Dollar.  [pdollar-at-gmail.com]
 Licensed under the Simplified BSD License [see external/bsd.txt]