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UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction

Authors
Leland McInnes, John Healy, James Melville
Year
2018
Preprint
Algorithm
UMAP
Source
arXiv
Identifiers
arXiv 1802.03426

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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Learning UMAP

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