%KLM Karhunen-Loeve Mapping (PCA or MCA of mean covariance matrix)% % 	[W,FRAC] = KLM(A,N)% 	[W,N]    = KLM(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% The Karhunen-Loeve Mapping performs a principal component analysis% (PCA) or minor component analysis (MCA) on the mean class covariance% matrix (weighted by the class prior probabilities). It finds a% rotation of the dataset A to an N-dimensional linear subspace such% that at least (for PCA) or at most (for MCA) a fraction FRAC of the% total variance is preserved.%% PCA is applied when N (or FRAC) >= 0; MCA when N (or FRAC) < 0. If N% is given (abs(N) >= 1), FRAC is optimised. If FRAC is given% (abs(FRAC) < 1), N is optimised. %% Objects in a new dataset B can be mapped by B*W, W*B or by% A*KLM([],N)*B.  Default (N = inf): the features are decorrelated and% ordered, but no feature reduction is performed.%% ALTERNATIVE%% 	V = KLM(A,0)% % Returns the cummulative fraction of the explained variance. V(N) is% the cumulative fraction of the explained variance by using N% eigenvectors.%% Use PCA for a principal component analysis on the total data% covariance.  Use FISHERM for optimizing the linear class% separability (LDA).%% This function is basically a wrapper around pcaklm.m.% % SEE ALSO% MAPPINGS, DATASETS, PCAKLM, PCLDC, KLLDC, PCA, FISHERM% Copyright: R.P.W. Duin, r.p.w.duin@prtools.org% Faculty EWI, Delft University of Technology% P.O. Box 5031, 2600 GA Delft, The Netherlands% $Id: klm.m,v 1.11 2005/03/04 10:33:44 duin Exp $function [w,truefrac] = klm (varargin)	prtrace(mfilename);	[w,truefrac] = pcaklm(mfilename,varargin{:});return