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Interpretable Locally Adaptive Nearest Neighbors

8 November 2020
Jan Philip Göpfert
H. Wersing
Barbara Hammer
ArXiv (abs)PDFHTML
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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