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

TL;DR

This paper explores variants of the Wasserstein distance tailored for classifying 2D curves by emphasizing specific fragments, demonstrated through archaeological data clustering experiments.

Contribution

It introduces new Wasserstein distance variants that focus on important curve fragments, enhancing classification relevance.

Findings

Effective clustering of archaeological 2D curves using the proposed Wasserstein variants.

Focus on key curve fragments improves classification accuracy.

Experimental validation confirms the approach's usefulness.

Abstract

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.