Learning performance of uncentered kernel-based principal component analysis

Principal component analysis (PCA) may be the most popular dimension reduction method. In this paper, the learning scheme of kernel PCA methods is established. Moreover, for the uncentered case, we introduce the error representation, and prove the comparison theorem that the learning error can be bounded by the excess generalization error. Under the condition that the positive eigenvalues of L-K are all single, the satisfied error bound O(n(-1|2)) is deduced.