UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction

Leland McInnes, John Healy

2018 arXiv.org Cited 11,796 times

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.

BibTeX

@article{McInnes2018,
  author = {McInnes, Leland and Healy, John and Melville, James},
  journal = {arXiv preprint arXiv:1802.03426},
  title = {Umap: Uniform manifold approximation and projection for dimension reduction},
  year = {2018},
}