Hyperbolic embeddings offer excellent quality with few dimensions when
embedding hierarchical data structures like synonym or type hierarchies. Given
a tree, we give a combinatorial construction that embeds the tree in hyperbolic
space with arbitrarily low distortion without using optimization. On WordNet,
our combinatorial embedding obtains a mean-average-precision of 0.989 with only
two dimensions, while Nickel et al.'s recent construction obtains 0.87 using
200 dimensions. We provide upper and lower bounds that allow us to characterize
the precision-dimensionality tradeoff inherent in any hyperbolic embedding. To
embed general metric spaces, we propose a hyperbolic generalization of
multidimensional scaling (h-MDS). We show how to perform exact recovery of
hyperbolic points from distances, provide a perturbation analysis, and give a
recovery result that allows us to reduce dimensionality. The h-MDS approach
offers consistently low distortion even with few dimensions across several
datasets. Finally, we extract lessons from the algorithms and theory above to
design a PyTorch-based implementation that can handle incomplete information
and is scalable.

该研究提出了一种嵌入树形数据结构的超 bolic embeddings 算法，无需优化即可实现任意低失真，同时提供了 h-MDS 方法来嵌入广泛的度量空间以及可处理不完整信息和可扩展的 PyTorch 实现。