A Surprisingly Simple Method for Distributed Euclidean-Minimum Spanning Tree / Single Linkage Dendrogram Construction from High Dimensional Embeddings via Distance Decomposition
<|reference_start|>A Surprisingly Simple Method for Distributed Euclidean-Minimum Spanning Tree / Single Linkage Dendrogram Construction from High Dimensional Embeddings via Distance Decomposition: 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.<|reference_end|>
@article{lettich2024a,
    title={A Surprisingly Simple Method for Distributed Euclidean-Minimum Spanning
  Tree / Single Linkage Dendrogram Construction from High Dimensional
  Embeddings via Distance Decomposition},
    author={Richard Lettich},
    journal={arXiv preprint arXiv:2406.01739},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.01739},
    primaryClass={cs.DC cs.CG cs.DS}
}