This is the authors' abstract. We don't add key points for this paper.
Uniform Manifold Approximation and Projection (UMAP) is a dimension reduction technique that can be used for visualisation similarly to t-SNE, but also for general non-linear dimension reduction. UMAP has a rigorous mathematical foundation, but is simple to use, with a scikit-learn compatible API. UMAP is among the fastest manifold learning implementations available -significantly faster than most t-SNE implementations.
Answers are grounded in this paper and cite their sources.
No takes yet. Share an insight, caveat, or question.
McInnes et al. (2018) studied this question.
Synapse has enriched 10 related papers. Consider them for comparative context: