Interpretable Locally Adaptive Nearest Neighbors
Machine Learning
2021-09-30 v2 Machine Learning
Abstract
When training automated systems, it has been shown to be beneficial to adapt the representation of data by learning a problem-specific metric. This metric is global. We extend this idea and, for the widely used family of k nearest neighbors algorithms, develop a method that allows learning locally adaptive metrics. These local metrics not only improve performance but are naturally interpretable. To demonstrate important aspects of how our approach works, we conduct a number of experiments on synthetic data sets, and we show its usefulness on real-world benchmark data sets.
Cite
@article{arxiv.2011.03904,
title = {Interpretable Locally Adaptive Nearest Neighbors},
author = {Jan Philip Göpfert and Heiko Wersing and Barbara Hammer},
journal= {arXiv preprint arXiv:2011.03904},
year = {2021}
}