Probability Metrics for Tropical Spaces of Different Dimensions

TL;DR

This paper introduces a novel Wasserstein distance for comparing probability distributions across different tropical spaces, enabling applications like phylogenetic tree analysis with varying leaf sets.

Contribution

It develops a tropical geometry-based framework for measuring differences between measures on spaces of different dimensions, extending existing methods.

Findings

Proves the equivalence of directionality in tropical mappings.

Provides a practical computational approach for real data.

Enables comparison of phylogenetic trees with different leaf sets.

Abstract

The problem of comparing probability distributions is at the heart of many tasks in statistics and machine learning. Established comparison methods treat the standard setting that the distributions are supported in the same space. Recently, a new geometric solution has been proposed to address the more challenging problem of comparing measures in Euclidean spaces of differing dimensions. Here, we study the same problem of comparing probability distributions of different dimensions in the tropical setting, which is becoming increasingly relevant in applications involving complex data structures such as phylogenetic trees. Specifically, we construct a Wasserstein distance between measures on different tropical projective tori -- the focal metric spaces in both theory and applications of tropical geometry -- via tropical mappings between probability measures. We prove equivalence of the directionality of the maps, whether mapping from a low dimensional space to a high dimensional space or vice versa. As an important practical implication, our work provides a framework for comparing probability distributions on the spaces of phylogenetic trees with different leaf sets. We demonstrate the computational feasibility of our approach using existing optimisation techniques on both simulated and real data.