Interpretable Locally Adaptive Nearest Neighbors

NEUROCOMPUTING

卷:470

Article

Goepfert, Jan Philip¹  Wersing, Heiko²  Hammer, Barbara¹ 

[1] Bielefeld Univ, Bielefeld, Germany

[2] Honda Res Inst Europe GmbH, Offenbach, Germany

关键词: Interpretable machine learning;    Metric learning;    Nearest neighbors;   

DOI  :  10.1016/j.neucom.2021.05.105

来源: Elsevier

【 摘 要 】

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. (c) 2021 Elsevier B.V. All rights reserved.

【 授权许可】

Free