  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 the size of the training data,
resulting in lower storage consumption and computational complexity.
Furthermore, the method can be regarded as a regularization technique, which
improves the generalizability of learned target functions. We demonstrate
significant improvements in comparison to the computation of data-driven
predictions involving the full training data set. The method is applied to
classification and regression problems from different application areas such as
image recognition, system identification, and oceanographic time series
analysis.
