Umap: Uniform manifold approximation and projection for dimension reduction
L McInnes, J Healy, J Melville - arXiv preprint arXiv:1802.03426, 2018 - arxiv.org
L McInnes, J Healy, J Melville
arXiv preprint arXiv:1802.03426, 2018•arxiv.orgUMAP (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 …
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 …
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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