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. The algorithm is founded on three assumptions about the data:
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The data is uniformly distributed on a Riemannian manifold;
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The Riemannian metric is locally constant (or can be approximated as such);
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The manifold is locally connected.
From these assumptions it is possible to model the manifold with a fuzzy topological structure. The embedding is found by searching for a low dimensional projection of the data that has the closest possible equivalent fuzzy topological structure. GitHub - lmcinnes/umap: Uniform Manifold Approximation and Projection
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