Face Recognition Based on Supervised Kernel Isomap
Gu Ruijun, Wenbo Xu · 2006
Several novel methods for nonlinear dimensionality reduction, named as manifold learning, have been proposed recently and widely used in pattern recognition and machine learning. In this paper, we present three face recognition methods based on kernel Isomap, which is a representative manifold learning method using kernel trick. Considering the class label by adjusting the Euclidean distance using weight factor w, both SK-Isomap-I and SK-Isomap-II are supervised and perform better than original K-Isomap. Unlike SK-Isomap-I, SK-Isomap-II utilizes nearest class center instead ofKNN to determine class label of a test data. The experimental results showed that SK-Isomap-II performed the best in three of them and the error rate of SK-Isomap-II was only about 50% of K-Isomap