A simple feature extraction for high dimensional image representations

We investigate a method to find local clusters in low dimensional subspaces of high dimensional data, e.g. in high dimensional image descriptions. Using cluster centers instead of the full set of data will speed up the performance of learning algorithms for object recognition, and will possibly also improve performance because overfitting might be avoided.