lcovPca2: Improved Principal Component Analysis on a covariance object
Description
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
Usage
lcovPca2(lcov, dimRange = NULL)
Arguments
lcov
A list that contains all information about
the handled covariance-structure
dimRange
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
Value
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.