Kernel fusionrefinement for semi-supervised nonlinear dimension reduction
A nonlinear semi-supervised dimension reduction method is developed. Minimal loss of information and a nonlinear model are synthesized in the method. A theorem for our novel kernel fusionrefinement method is proposed and proved. An algorithm to implement our kernel fusionrefinement method is developed. The results from extensive experiments justify the effectiveness of our method.
kernel fusionrefinement dimension reduction semi-supervised learning