Stochastic Neighbor Embedding separates well-separated clusters

Stochastic Neighbor Embedding and its variants are widely used dimensionality reduction techniques -- despite their popularity, no theoretical results are known. We prove that the optimal SNE embedding of well-separated clusters from high dimensions to the real line $\mathbb{R}$ manages to successfully separate the clusters in a quantitative way.