UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction. 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.
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References in zbMATH (referenced in 2 articles , 1 standard article )
Showing results 1 to 2 of 2.
- Isotta Landi, Veronica Mandelli, Michael V. Lombardo: reval: a Python package to determine the best number of clusters with stability-based relative clustering validation (2020) arXiv
- Leland McInnes, John Healy, James Melville: UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction (2018) arXiv