[1802.03426] UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction

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.

UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction Leland McInnes arXiv:1802.03426v3 [stat.ML] 18 Sep 2020 Tutte Institute for Mathematics and Computing leland.mcinnes@gmail.com John Healy…

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