  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.
