# Interpretable Locally Adaptive Nearest Neighbors

arXiv: https://arxiv.org/abs/2011.03904 · HTML: https://mlsanity.com/paper/2011.03904/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Nov 2020.

## Built on by (most cited first, 1 of 1)

- [Subspace Learning Machine (SLM): Methodology and Performance](https://mlsanity.com/paper/2205.05296/) (2022)

## Abstract (arXiv)

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.