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.
@article{arxiv.1802.03426,
title = {UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction},
author = {Leland McInnes and John Healy and James Melville},
journal= {arXiv preprint arXiv:1802.03426},
year = {2020}
}
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Reference implementation available at http://github.com/lmcinnes/umap