mlsanityData to 7 Oct 2026

Barnes-Hut-SNE

arXiv 1301.3342 ↗First posted 15 Jan 2013cs.LG cs.CV stat.MLcited by 14

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Abstract (arXiv)

The paper presents an O(N log N)-implementation of t-SNE -- an embedding technique that is commonly used for the visualization of high-dimensional data in scatter plots and that normally runs in O(N^2). The new implementation uses vantage-point trees to compute sparse pairwise similarities between the input data objects, and it uses a variant of the Barnes-Hut algorithm - an algorithm used by astronomers to perform N-body simulations - to approximate the forces between the corresponding points in the embedding. Our experiments show that the new algorithm, called Barnes-Hut-SNE, leads to substantial computational advantages over standard t-SNE, and that it makes it possible to learn embeddings of data sets with millions of objects.

Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). First posted on arXiv 15 Jan 2013.

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Citing papers per year: 2021: 2; 2022: 4; 2023: 2; 2024: 2; 2025: 2; 2026: 2.

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