lcovPca2: Improved Principal Component Analysis on a covariance object

In rSFA: Slow Feature Analysis

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.

Note

lcovFix(lcov) has to be used before this function is applied

See Also

lcovFix lcovPca

rSFA documentation built on March 29, 2022, 5:05 p.m.