I think there are some mistake in this implementation, the last step the feature vector feature dimension reduction procedure is incorrect, since you can not do it in this way. If you do it in this way, how can you tell the difference between PCA and KPCA. we should do it by using inner product form.
02 Sep 2011
Non-linear dimension reduction using kernel PCA.
Sorry Enrique I don't understand your second point. Surely it doesn't matter whether you normalize just the non-zero eigenvalues or all of the eigenvalues since the non-zero eigenvalues won't change the projections? Have I missed something? Thanks.