A Surprisingly Simple Method for Distributed Euclidean-Minimum Spanning Tree / Single Linkage Dendrogram Construction from High Dimensional Embeddings via Distance Decomposition

TL;DR

The paper presents a simple decomposition method for efficiently computing exact Euclidean Minimum Spanning Trees in high-dimensional spaces, enabling scalable clustering and dendrogram construction from neural network embeddings.

Contribution

It introduces a novel decomposition approach for distributed calculation of geometric minimum spanning trees in high dimensions, addressing limitations of existing low-dimensional algorithms.

Findings

Enables exact MST computation in high-dimensional embeddings

Facilitates efficient construction of single linkage dendrograms

Applicable to neural network embedding clustering

Abstract

We introduce a decomposition method for the distributed calculation of exact Euclidean Minimum Spanning Trees in high dimensions (where sub-quadratic algorithms are not effective), or more generalized geometric-minimum spanning trees of complete graphs, where for each vertex $v\in V$ in the graph $G=(V,E)$ is represented by a vector in $\vec{v}\in \mathbb{R}^n$, and each for any edge, the the weight of the edge in the graph is given by a symmetric binary `distance' function between the representative vectors $w(\{x,y\}) = d(\vec{x},\vec{y})$. This is motivated by the task of clustering high dimensional embeddings produced by neural networks, where low-dimensional algorithms are ineffective; such geometric-minimum spanning trees find applications as a subroutine in the construction of single linkage dendrograms, as the two structures can be converted between each other efficiently.