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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
1. The data is uniformly distributed on Riemannian manifold;
2. The Riemannian metric is locally constant (or can be approximated as such);
3. 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**
Conda install, via the excellent work of the conda-forge team:
.. code:: bash
conda install -c conda-forge umap-learn
The conda-forge packages are available for linux, OS X, and Windows 64 bit.
PyPI install, presuming you have numba and sklearn and all its requirements
(numpy and scipy) installed:
.. code:: bash
pip install umap-learn
.. toctree::
:maxdepth: 2
:caption: User Guide / Tutorial:
basic_usage
parameters
transform
supervised
clustering
auto_examples/index
faq
.. toctree::
:maxdepth: 2
:caption: Background on UMAP:
dimension_reduction
how_umap_works
benchmarking
.. toctree::
:caption: API Reference:
api
Indices and tables
==================
* :ref:`genindex`
* :ref:`modindex`
* :ref:`search`