Towards an Analytical Definition of Sufficient Data

Abstract

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.

Class Density and Dataset Quality in High-Dimensional, Unstructured Data

“…In this work, we will be defining a density measurement that can be applied to the 3-dimensional reduction of each class in a dataset independently and then providing a study of classification accuracy after reducing the data in each class to a variety of target densities as opposed to reducing each class by the same amount as in [9].…”