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Accelerating t-SNE using tree-based algorithms
Abstract
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.
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Metadata
- Authors
- van der Maaten, L.
- Published
- 2014-01-01
- Volume
- 15
- Issue
- 1
- Pages
- 3221-3245
- DOI
- 10.5555/2627435.2697068
- OpenAlex
- W1875842236
- Cited by
- 2,226
- References
- 5
- FWCI
- 73.28
- Type
- journal article
- Language
- en
- Open access
- closed
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