# Near Isometric Terminal Embeddings for Doubling Metrics

arXiv: https://arxiv.org/abs/1802.07967 · HTML: https://mlsanity.com/paper/1802.07967/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Feb 2018.

## Built on by (most cited first, 1 of 1)

- [Neural Spacetimes for DAG Representation Learning](https://mlsanity.com/paper/2408.13885/) (2024)

## Abstract (arXiv)

Given a metric space (X,d), a set of terminals Ksubseteq X, and a parameter t≥ 1, we consider metric structures (e.g., spanners, distance oracles, embedding into normed spaces) that preserve distances for all pairs in K× X up to a factor of t, and have small size (e.g. number of edges for spanners, dimension for embeddings). While such terminal (aka source-wise) metric structures are known to exist in several settings, no terminal spanner or embedding with distortion close to 1, i.e., t=1+ε for some small 0<ε<1, is currently known. Here we devise such terminal metric structures for em doubling metrics, and show that essentially any metric structure with distortion 1+ε and size s(|X|) has its terminal counterpart, with distortion 1+O(ε) and size s(|K|)+1. In particular, for any doubling metric on n points, a set of k=o(n) terminals, and constant 0<ε<1, there exists: (1) A spanner with stretch 1+ε for pairs in K× X, with n+o(n) edges. (2) A labeling scheme with stretch 1+ε for pairs in K× X, with label size ≈ log k. (3) An embedding into ℓ_∞d with distortion 1+ε for pairs in K× X, where d=O(log k). Moreover, surprisingly, the last two results apply if only K is a doubling metric, while X can be arbitrary.