Accelerating t-SNE using tree-based algorithms

The paper investigates the acceleration of t-SNE—an embedding technique that is com-monly 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 em-beddings in O(N logN). 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.