  t-Distributed Stochastic Neighbor Embedding (t-SNE) is one of the most widely
used dimensionality reduction methods for data visualization, but it has a
perplexity hyperparameter that requires manual selection. In practice, proper
tuning of t-SNE perplexity requires users to understand the inner working of
the method as well as to have hands-on experience. We propose a model selection
objective for t-SNE perplexity that requires negligible extra computation
beyond that of the t-SNE itself. We empirically validate that the perplexity
settings found by our approach are consistent with preferences elicited from
human experts across a number of datasets. The similarities of our approach to
Bayesian information criteria (BIC) and minimum description length (MDL) are
also analyzed.
