%KLLDC Linear classifier built on the KL expansion of the common covariance matrix% %  W = KLLDC(A,N)%  W = KLLDC(A,ALF)% % INPUT%  A    Dataset%  N    Number of significant eigenvectors %  ALF  0 < ALF <= 1, percentage of the total variance explained (default: 0.9)%% OUTPUT%  W    Linear classifier %% DESCRIPTION  % Finds the linear discriminant function W for the dataset A. This is done  % by computing the LDC on the data projected on the first eigenvectors of% the averaged covariance matrix of the classes.  Either first N eigenvectors% are used or the number of eigenvectors is determined such that ALF, the % percentage of the total variance is explained. (Karhunen Loeve expansion)%%	SEE ALSO%	MAPPINGS, DATASETS, PCLDC, KLM, FISHERM% 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: klldc.m,v 1.8 2003/11/22 23:20:38 bob Exp $function W = klldc(a,n)	prtrace(mfilename);	if nargin < 2		n = [];		prwarning(4,'number of significant eigenvectors not supplied, 0.9 variance explained');	end	if nargin == 0 | isempty(a)		W = mapping('klldc',{n});		W = setname(W,'KL Bayes-Normal-1');		return;	end	islabtype(a,'crisp','soft');	isvaldset(a,2,2); % at least 2 object per class, 2 classes	v = klm(a,n);	W = v*ldc(a*v);	W = setname(W,'KL Bayes-Normal-1');	W = setcost(W,a);	return