%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 Netherlands function W = nlfisherm(a,n) if nargin == 1, n = []; end if nargin == 0 | isempty(a) W = mapping('nlfisherm',n); return end [nlab,lablist,m,k,c,p,fl,imheight] = dataset(a); if isempty(n), n = min(k,c)-1; end if n >= m | n >= c error('Dataset too small or or has too few classes for demanded output dimensionality') end w = klms(a); a = a*w; k = size(a,2); if isempty(n), n = k; end if n >= m error('Dataset too small or singular for demanded output dimensionality') end u = 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 end end [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);
