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Uniform Manifold Approximation and Projection

In: Applied Multivariate Statistical Analysis

Author

Listed:
  • Wolfgang Karl Härdle

    (Humboldt-Universität zu Berlin, Ladislaus von Bortkiewicz Chair of Statistics)

  • Léopold Simar

    (Université Catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences)

  • Matthias R. Fengler

    (University of St. Gallen, School of Economics and Political Science)

Abstract

Uniform manifold approximation and projection (UMAP) is among the most highly appraised and most powerful procedures for data visualization and dimension reduction. Despite having been proposed fairly recently, UMAP has immediately excited the entire community. UMAP was applied successfully in high-dimensional applications of image processing, natural language processing, and biology, particularly in single-cell analysis. Since its introduction, the principle ideas of UMAP have undergone further refinement and customization, in order to accommodate the objectives in further areas of applied data science.

Suggested Citation

  • Wolfgang Karl Härdle & Léopold Simar & Matthias R. Fengler, 2024. "Uniform Manifold Approximation and Projection," Springer Books, in: Applied Multivariate Statistical Analysis, edition 0, chapter 0, pages 581-595, Springer.
    • Handle: RePEc:spr:sprchp:978-3-031-63833-6_23
    DOI: 10.1007/978-3-031-63833-6_23
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