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We provide a definition for class density that can be used to measure theaggregate similarity of the samples within each of the classes in ahigh-dimensional, unstructured dataset. We then put forth several candidatemethods for calculating class density and analyze the correlation between thevalues each method produces with the corresponding individual class testaccuracies achieved on a trained model. Additionally, we propose a definitionfor dataset quality for high-dimensional, unstructured data and show that thosedatasets that met a certain quality threshold (experimentally demonstrated tobe > 10 for the datasets studied) were candidates for eliding redundant databased on the individual class densities.

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