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Description

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: the data is uniformly distributed on Riemannian manifold; the Riemannian metric is locally constant (or can be approximated as such); 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. The details for the underlying mathematics can be found in `our paper on ArXiv <https://arxiv.org/abs/1802.03426>`_: McInnes, L, Healy, J, *UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction*, ArXiv e-prints 1802.03426, 2018

Repository

https://github.com/lmcinnes/umap.git

Project Slug

umap-learn

Last Built

1 day, 15 hours ago passed

Maintainers

Home Page

https://github.com/lmcinnes/umap

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Tags

visualization, dimension-reduction, t-sne, umap, manifold-learning

Short URLs

umap-learn.readthedocs.io
umap-learn.rtfd.io

Default Version

latest

'latest' Version

master