“UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction”, 2018-02-09 (; backlinks; similar):
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.