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 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.
CITATION STYLE
Van Der Maaten, L. (2015). Accelerating t-SNE using tree-based algorithms. Journal of Machine Learning Research, 15, 3221–3245.
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