  The nearest prototype classification is a less computationally intensive
replacement for the $k$-NN method, especially when large datasets are
considered. In metric spaces, centroids are often used as prototypes to
represent whole clusters. The selection of cluster prototypes in non-metric
spaces is more challenging as the idea of computing centroids is not directly
applicable.
  In this paper, we present CRS, a novel method for selecting a small yet
representative subset of objects as a cluster prototype. Memory and
computationally efficient selection of representatives is enabled by leveraging
the similarity graph representation of each cluster created by the NN-Descent
algorithm. CRS can be used in an arbitrary metric or non-metric space because
of the graph-based approach, which requires only a pairwise similarity measure.
As we demonstrate in the experimental evaluation, our method outperforms the
state of the art techniques on multiple datasets from different domains.
