Performs PCA _and_ whitening on the covariance object referenced by lcov. Difference to LCOV_PCA: null the rows of W (columns of DW) where the corresponding eigenvalue in D is close to zero (more precisely: if lam/lam_max < EPS = 1e-7). This is numerically stable in the case where the covariance matrix is singular. - Author: Wolfgang Konen, Cologne Univ., May'2009
lcovPca2(lcov, dimRange = NULL)A list that contains all information about the handled covariance-structure
A number or vector for dimensionality reduction: if it is a number: only the first components 1:dimRange are kept (those with largest eigenvalues) if it is a range: only the components in the range dimRange[1]..dimRange[2] are kept
returns a list: $W is the whitening matrix, $DW the dewhitening matrix and $D an array containing a list of the eigenvalues. $kvar contains the total variance kept in percent.