Coarse embeddability of Wasserstein space and the space of persistence diagrams

TL;DR

This paper establishes an equivalence between the embeddability properties of persistence diagram spaces and Wasserstein spaces, highlighting open questions about their coarse embeddability into Hilbert spaces for certain metrics.

Contribution

It proves that embeddability of persistence diagrams is equivalent to that of Wasserstein space on b5^2, clarifying the relationship between these metric spaces.

Findings

Embeddability of persistence diagrams is equivalent to that of Wasserstein space on b5^2.

Wasserstein space on b5^2 is snowflake universal for p > 1.

Open questions remain about coarse embeddability for p between 1 and 2.

Abstract

We prove an equivalence between open questions about the embeddability of the space of persistence diagrams and the space of probability distributions (i.e.~Wasserstein space). It is known that for many natural metrics, no coarse embedding of either of these two spaces into Hilbert space exists. Some cases remain open, however. In particular, whether coarse embeddings exist with respect to the $p$-Wasserstein distance for $1\leq p\leq 2$ remains an open question for the space of persistence diagrams and for Wasserstein space on the plane. In this paper, we show that embeddability for persistence diagrams \redd{is equivalent to} embeddability for Wasserstein space on $\mathbb{R}^2$. \redd{When $p > 1$, Wasserstein space on $\mathbb{R}^2$ is snowflake universal (an obstruction to embeddability into any Banach space of non-trivial type) if and only if the space of persistence diagrams is snowflake universal.