UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction

Leland McInnes; John Healy; James Melville

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arXiv · 1802.03426

UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction

Leland McInnes·John Healy·James Melville

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Abstract

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.

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Leland McInnes, John Healy, James Melville. 2018-02-09. UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction. https://arxiv.org/abs/1802.03426

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