Feature space approximation for kernel-based supervised learning

We propose a method for the approximation of high- or even infinite-dimensional feature vectors, which play an important role in supervised learning. The goal is to reduce size training data, resulting lower storage consumption and computational complexity. Furthermore, can be regarded as regularization technique, improves generalizability learned target functions. demonstrate significant improvements comparison computation data-driven predictions involving full data set. applied classification regression problems from different application areas such image recognition, system identification, oceanographic time series analysis.