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Visualizing data using t-sne
Laurens van der Maaten and Geoffrey Hinton · 2008
Earlier work this paper cites.
Accelerating t-sne using tree-based algorithms
Laurens van der Maaten · 2014
Earlier work this paper cites.

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Stochastic Neighbor Embedding separates well-separated clusters
Original
Uri Shaham and Stefan Steinerberger · 2017
Closest in time.

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2017

Clustering with t-SNE, provably

Linderman, George C., Steinerberger, Stefan

Understand

t-distributed Stochastic Neighborhood Embedding (t-SNE), a clustering and visualization method proposed by van der Maaten & Hinton in 2008, has rapidly become a standard tool in a number of natural sciences.

Despite its overwhelming success, there is a distinct lack of mathematical foundations and the inner workings of the algorithm are not well understood.
The purpose of this paper is to prove that t-SNE is able to recover well-separated clusters; more precisely, we prove that t-SNE in the `early exaggeration' phase, an optimization technique proposed by van der Maaten & Hinton (2008) and van der Maaten (2014), can be rigorously analyzed.
As a byproduct, the proof suggests novel ways for setting the exaggeration parameter $\alpha$ and step size $h$.