spreadgda.m

通过核来泛化的判别分析（GDA）代码

function projectedVectors=SpreadGDA(T,L,dataGDA);
% Spread test vectors T into the GDA discriminant subspace.
% L is the learning vectors (like for buildGDA).
% dataGDA must be the output from buildGDA.
% T and L use line vectors

% Gaston Baudat & Fatiha Anouar / 21st October 2000 / Exton PA 19341 USA
% Designed under MatLab for Windows version 5.2.0.3084


n=length(T(:,1));	%size of the test set
m=length(L(:,1));	%size of the learning set

KernelEva=zeros(n,m);

for i=1:n
   tmp=zeros(1,m);
   for j=1:m
      tmp(j)=KernelFunction(T(i,:),L(j,:));
   end
   KernelEva(i,:)=tmp-sum(tmp)/m;
end

inter=dataGDA.sumK-sum(dataGDA.sumK)/m;
for i=1:n
   KernelEva(i,:)=KernelEva(i,:)-inter;
end
projectedVectors=KernelEva*dataGDA.norAlpha;