# Two-Stage Metric Learning

arXiv: https://arxiv.org/abs/1405.2798 · HTML: https://mlsanity.com/paper/1405.2798/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 May 2014.

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

- [Cartan moving frames and the data manifolds](https://mlsanity.com/paper/2409.12057/) (2024)

## Abstract (arXiv)

In this paper, we present a novel two-stage metric learning algorithm. We first map each learning instance to a probability distribution by computing its similarities to a set of fixed anchor points. Then, we define the distance in the input data space as the Fisher information distance on the associated statistical manifold. This induces in the input data space a new family of distance metric with unique properties. Unlike kernelized metric learning, we do not require the similarity measure to be positive semi-definite. Moreover, it can also be interpreted as a local metric learning algorithm with well defined distance approximation. We evaluate its performance on a number of datasets. It outperforms significantly other metric learning methods and SVM.