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arXiv · 1912.01098

Using Dimensionality Reduction to Optimize t-SNE

Rikhav Shah·Sandeep Silwal
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Abstract

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.

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Rikhav Shah, Sandeep Silwal. 2019-12-02. Using Dimensionality Reduction to Optimize t-SNE. https://arxiv.org/abs/1912.01098

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