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arXiv · 2011.03904

Interpretable Locally Adaptive Nearest Neighbors

Jan Philip Göpfert·Heiko Wersing·Barbara Hammer
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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.

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BibTeXRIS

Jan Philip Göpfert, Heiko Wersing, Barbara Hammer. 2020-11-08. Interpretable Locally Adaptive Nearest Neighbors. https://arxiv.org/abs/2011.03904

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