%KLMS Karhunen Loeve Mapping, followed by scaling
%
% [W,FRAC] = KLMS(A,N)
% [W,N] = KLMS(A,FRAC)
%
% INPUT
% A Dataset
% N or FRAC Number of dimensions (>= 1) or fraction of variance (< 1)
% to retain; if > 0, perform PCA; otherwise MCA. Default: N = inf.
%
% OUTPUT
% W Affine Karhunen-Loeve mapping
% FRAC or N Fraction of variance or number of dimensions retained.
%
% DESCRIPTION
% First a Karhunen Loeve Mapping is performed (i.e. PCA or MCA on the average
% prior-weighted class covariance matrix). The result is scaled by the mean
% class standard deviations. For N and FRAC, see KLM.
%
% Default N: select all ('pre-whiten' the average covariance matrix, i.e.
% orthogonalize and scale). The resulting mapping has a unit average
% covariance matrix.
%
% SEE ALSO (<a href="http://37steps.com/prtools">PRTools Guide</a>)
% MAPPINGS, DATASETS, KLM, PCA
% Copyright: R.P.W. Duin, duin@ph.tn.tudelft.nl
% Faculty of Applied Physics, Delft University of Technology
% P.O. Box 5046, 2600 GA Delft, The Netherlands
% $Id: klms.m,v 1.2 2006/03/08 22:06:58 duin Exp $
function [w,truefrac] = klms(a,n)
if (nargin < 2), n = []; end;
if (nargin < 1) | (isempty(a))
w = prmapping('klms',n);
w = setname(w,'Scaled KL Mapping');
return
end
[w,truefrac] = klm(a,n); % Calculate KL mapping
b = a*w; % Combine KL mapping with scaling on
w = w*scalem(b,'c-variance'); % KL-mapped data
w = setname(w,'Scaled KL Mapping');
return