Works as advertised! To train a linear SVM in the Nystrom feature-space, replace the computation of G and Ktilde at the end of INys() with:
M = Ve(:,pidx) * inVa;
Mdata = E * M;
then train SVM with Mdata (which has all training samples in the new feature space). To compute the same features for a test vector z, use:
Mz = exp(-sqdist(z', center')/kernel.para) * M;
and classify with the same linear SVM.