# Uncertainty Estimates for Ordinal Embeddings

arXiv: https://arxiv.org/abs/1906.11655 · HTML: https://mlsanity.com/paper/1906.11655/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jun 2019.

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

- [Active metric learning and classification using similarity queries](https://mlsanity.com/paper/2202.01953/) (2022)
- [Active Learning of Ordinal Embeddings: A User Study on Football Data](https://mlsanity.com/paper/2207.12710/) (2022)

## Abstract (arXiv)

To investigate objects without a describable notion of distance, one can gather ordinal information by asking triplet comparisons of the form "Is object x closer to y or is x closer to z?" In order to learn from such data, the objects are typically embedded in a Euclidean space while satisfying as many triplet comparisons as possible. In this paper, we introduce empirical uncertainty estimates for standard embedding algorithms when few noisy triplets are available, using a bootstrap and a Bayesian approach. In particular, simulations show that these estimates are well calibrated and can serve to select embedding parameters or to quantify uncertainty in scientific applications.