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

UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction