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UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction¶

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

You can find the software on github.

Installation

Install UMAP via pip:

Bash

pip install umap

This will install UMAP and all required dependencies. For development installation, see the README.

UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction¶

User Guide / Tutorial:¶

Background on UMAP:¶

Examples of UMAP usage¶

API Reference:¶

Development:¶

Indices and tables¶