Cluster Representatives Selection in Non-Metric Spaces for Nearest Prototype Classification
Abstract
The nearest prototype classification is a less computationally intensive replacement for the -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.
Cite
@article{arxiv.2107.01345,
title = {Cluster Representatives Selection in Non-Metric Spaces for Nearest Prototype Classification},
author = {Jaroslav Hlaváč and Martin Kopp and Jan Kohout},
journal= {arXiv preprint arXiv:2107.01345},
year = {2021}
}