%NLFISHERM Non-linear Fisher Mapping% % 	W = nlfisherm(A,n)% % Finds a mapping of the labeled dataset A to a n-dimensional  % linear subspace emphasizing the class separability for neighboring % classes.% % See also datasets, mappings, fisherm, klm% Copyright: M. Loog, 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 Netherlandsfunction W = nlfisherm(a,n)if nargin == 1,  n = []; endif nargin == 0 | isempty(a)        W = mapping('nlfisherm',n);        returnend[nlab,lablist,m,k,c,p,fl,imheight] = dataset(a);if isempty(n), n = min(k,c)-1; endif n >= m | n >= c        error('Dataset too small or or has too few classes for demanded output dimensionality')endw = klms(a);a = a*w;k = size(a,2);if isempty(n), n = k; endif n >= m        error('Dataset too small or singular for demanded output dimensionality')endu = meancov(a);d = +distm(u);e = 0.5*erf(sqrt(d)/(2*sqrt(2)));G = zeros(k,k);for j = 1:c	for i=j+1:c		G = G + p(i)*p(j)*e(i,j)*(u(j,:)-u(i,:))'*(u(j,:)-u(i,:))/d(i,j); % Marco Loog Mapping (wrong?)		G = (G + G')/2;		%G = G + (u(j,:)-u(i,:))'*(u(j,:)-u(i,:)); % about lda	endend[F,V] = eig(G); [v,I] = sort(-diag(V)); I = I(1:n);R = [F(:,I);-mean(a*F(:,I))];W = w*mapping('affine',R,[],k,n,1,imheight);