  t-SNE is a popular tool for embedding multi-dimensional datasets into two or
three dimensions. However, it has a large computational cost, especially when
the input data has many dimensions. Many use t-SNE to embed the output of a
neural network, which is generally of much lower dimension than the original
data. This limits the use of t-SNE in unsupervised scenarios. We propose using
\textit{random} projections to embed high dimensional datasets into relatively
few dimensions, and then using t-SNE to obtain a two dimensional embedding. We
show that random projections preserve the desirable clustering achieved by
t-SNE, while dramatically reducing the runtime of finding the embedding.
