# Active Metric Learning for Supervised Classification

arXiv: https://arxiv.org/abs/1803.10647 · HTML: https://mlsanity.com/paper/1803.10647/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Mar 2018.

## Built on by (most cited first, 2 of 2)

- [Accelerating ERM for data-driven algorithm design using output-sensitive techniques](https://mlsanity.com/paper/2204.03569/) (2022)
- [Exposing Query Identification for Search Transparency](https://mlsanity.com/paper/2110.07701/) (2021)

## Abstract (arXiv)

Clustering and classification critically rely on distance metrics that provide meaningful comparisons between data points. We present mixed-integer optimization approaches to find optimal distance metrics that generalize the Mahalanobis metric extensively studied in the literature. Additionally, we generalize and improve upon leading methods by removing reliance on pre-designated "target neighbors," "triplets," and "similarity pairs." Another salient feature of our method is its ability to enable active learning by recommending precise regions to sample after an optimal metric is computed to improve classification performance. This targeted acquisition can significantly reduce computational burden by ensuring training data completeness, representativeness, and economy. We demonstrate classification and computational performance of the algorithms through several simple and intuitive examples, followed by results on real image and medical datasets.