# On the application of the Wasserstein metric to 2D curves classification

arXiv: https://arxiv.org/abs/2601.07749 · HTML: https://mlsanity.com/paper/2601.07749/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 11 references that are in the map.
- First posted on arXiv 12 Jan 2026.

## Builds on (most cited first, 3 of 3)

- [Optimal Transport on Discrete Domains](https://mlsanity.com/paper/1801.07745/) (2018)
- [A comprehensive study of clustering a class of 2D shapes](https://mlsanity.com/paper/2111.06662/) (2021)
- [Constrained Mass Optimal Transport](https://mlsanity.com/paper/2206.13352/) (2022)

## Abstract (arXiv)

In this work we analyse a number of variants of the Wasserstein distance which allow to focus the classification on the prescribed parts (fragments) of classified 2D curves. These variants are based on the use of a number of discrete probability measures which reflect the importance of given fragments of curves. The performance of this approach is tested through a series of experiments related to the clustering analysis of 2D curves performed on data coming from the field of archaeology.