Interpretable Locally Adaptive Nearest Neighbors

• URL: http://arxiv.org/abs/2011.03904v2
• Date: Wed, 29 Sep 2021 17:50:18 GMT
• Title: Interpretable Locally Adaptive Nearest Neighbors
• Authors: Jan Philip G\"opfert and Heiko Wersing and Barbara Hammer
• Abstract summary: We develop a method that allows learning locally adaptive metrics. These local metrics not only improve performance but are naturally interpretable. We conduct a number of experiments on synthetic data sets, and show its usefulness on real-world benchmark data sets.
• Score: 8.052709336750821
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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