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UMAP: Uniform Manifold Approximation and Projection

Introduces UMAP, a fast, mathematically grounded manifold learning technique for visualization and general non-linear dimension reduction.

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UMAP: Uniform Manifold Approximation and Projection

By Leland McInnes, John Healy, Nathaniel Saul et al.Journal of Open Source Software
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UMAP (Uniform Manifold Approximation and Projection) is a dimension reduction technique that can be used for visualization similarly to t-SNE while also supporting general non-linear dimension reduction. It is built on a rigorous mathematical foundation but is designed to be simple to use, exposing a scikit-learn compatible API so it fits naturally into existing workflows.

UMAP is among the fastest manifold learning implementations available, running significantly faster than most t-SNE implementations. This combination of speed, a rigorous foundation, and an easy-to-use API is what distinguishes UMAP as a practical, general-purpose approach for reducing and visualizing high-dimensional data.

Abstract

UMAP is a dimension reduction technique usable for visualization, much like t-SNE, but also for general non-linear dimension reduction. It rests on a rigorous mathematical foundation yet remains simple to use through a scikit-learn compatible API. UMAP ranks among the fastest available manifold learning implementations, running significantly faster than most t-SNE implementations.

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dimensionality reductionmanifold learningdata visualizationUMAPt-SNE
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