Accelerating T-SNE Using Tree-Based Algorithms

Abstract

The paper investigates the acceleration of t-SNE--an embedding technique that is commonly used for the visualization of high- dimensional data in scatter plots--using two tree-based algorithms. In particular, the paper develops variants of the Barnes-Hut algorithm and of the dual-tree algorithm that approximate the gradient used for learning t-SNE embeddings in $\mathcal{O}(N \log N)$. Our experiments show that the resulting algorithms substantially accelerate t-SNE, and that they make it possible to learn embeddings of data sets with millions of objects. Somewhat counterintuitively, the Barnes-Hut variant of t-SNE appears to outperform the dual-tree variant.