An improved algorithm for kernel principal component analysis
Kernel principal component analysis (KPCA), introduced by Scholkopf et al., is a nonlinear generalization of the popular principal component analysis (PCA) via the kernel trick. KPCA has shown to be a very powerful approach of extracting nonlinear features for classification and regression applications. However, the standard KPCA algorithm (Scholkopf et al., 1998, Neural Computation 10, 1299-1319) may suffer from computational problem for large scale data set. To overcome these drawbacks, we propose an efficient training algorithm in this paper, and show that this approach is of much more computational efficiency compared to the previous ones for KPCA.
kernel principal component analysis eigenvalue decomposition feature extraction