# UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction

arXiv: https://arxiv.org/abs/1802.03426 · HTML: https://mlsanity.com/paper/1802.03426/

## Key facts

- Cited by 2,898 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Feb 2018.

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## Abstract (arXiv)

UMAP (Uniform Manifold Approximation and Projection) is a novel manifold learning technique for dimension reduction. UMAP is constructed from a theoretical framework based in Riemannian geometry and algebraic topology. The result is a practical scalable algorithm that applies to real world data. The UMAP algorithm is competitive with t-SNE for visualization quality, and arguably preserves more of the global structure with superior run time performance. Furthermore, UMAP has no computational restrictions on embedding dimension, making it viable as a general purpose dimension reduction technique for machine learning.