%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% 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 1.5 2003/10/07 11:58:37 bob Exp $function [w,truefrac] = klms(a,n)	prtrace(mfilename);	if (nargin < 2), n = []; end;	if (nargin < 1) | (isempty(a))		w = mapping('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