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:
McInnes, L, Healy, J, UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction, ArXiv e-prints 1802.03426, 2018
6 days, 3 hours ago passed
.. image:: https://readthedocs.org/projects/umap-learn/badge/?version=latest :target: https://umap-learn.readthedocs.io/en/latest/?badge=latest :alt: Documentation Status
<a href='https://umap-learn.readthedocs.io/en/latest/?badge=latest'> <img src='https://readthedocs.org/projects/umap-learn/badge/?version=latest' alt='Documentation Status' /> </a>
Project Privacy Level