mlsanityData to 7 Oct 2026

UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction

arXiv 1802.03426 ↗First posted 9 Feb 2018stat.ML cs.CG cs.LGcited by 2,841

Not on the map. Why?

Cited by 71 in Diffusion models, but placed by its own abstract, which didn’t fit.

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.

Key facts. Cited by 2,841 papers in the map (data through 7 Oct 2026). First posted on arXiv 9 Feb 2018.

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Citing papers per year: 2019: 1; 2020: 0; 2021: 275; 2022: 332; 2023: 411; 2024: 558; 2025: 689; 2026: 575.

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