  Prototype methods seek a minimal subset of samples that can serve as a
distillation or condensed view of a data set. As the size of modern data sets
grows, being able to present a domain specialist with a short list of
"representative" samples chosen from the data set is of increasing
interpretative value. While much recent statistical research has been focused
on producing sparse-in-the-variables methods, this paper aims at achieving
sparsity in the samples. We discuss a method for selecting prototypes in the
classification setting (in which the samples fall into known discrete
categories). Our method of focus is derived from three basic properties that we
believe a good prototype set should satisfy. This intuition is translated into
a set cover optimization problem, which we solve approximately using standard
approaches. While prototype selection is usually viewed as purely a means
toward building an efficient classifier, in this paper we emphasize the
inherent value of having a set of prototypical elements. That said, by using
the nearest-neighbor rule on the set of prototypes, we can of course discuss
our method as a classifier as well.
