  We show that, for each of five datasets of increasing complexity, certain
training samples are more informative of class membership than others. These
samples can be identified a priori to training by analyzing their position in
reduced dimensional space relative to the classes' centroids. Specifically, we
demonstrate that samples nearer the classes' centroids are less informative
than those that are furthest from it. For all five datasets, we show that there
is no statistically significant difference between training on the entire
training set and when excluding up to 2% of the data nearest to each class's
centroid.
