lcovPca2 {rSFA} | R Documentation |
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: |
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.
Note
lcovFix(lcov) has to be used before this function is applied