UMAP is a novel, scalable manifold learning algorithm for dimension reduction that competes with t-SNE while preserving more global structure and having no embedding dimension restrictions.
Laplacian eigenmaps for dimen- sionality reduction and data representation
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UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction
UMAP is a novel, scalable manifold learning algorithm for dimension reduction that competes with t-SNE while preserving more global structure and having no embedding dimension restrictions.