UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction

TLDR

UMAP is a novel manifold learning technique for dimensionality reduction. UMAP is built on a theoretical framework grounded in Riemannian geometry and algebraic topology. The resulting algorithm is practical, scalable, and competitive with t‑SNE for visualization quality while preserving more global structure, running faster, and imposing no limits on embedding dimension, making it a general‑purpose dimensionality‑reduction technique.

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.