Distributional Principal Autoencoder

Project description

Distributional Principal Autoencoder

Distributional Principal Autoencoder (DPA) is a nonlinear dimension reduction method proposed in the paper "Distributional Principal Autoencoders" by Xinwei Shen and Nicolai Meinshausen. This directory contains the Python implementation of DPA.

Installation

The latest release of the Python package can be installed through pip:

pip install DistributionalPrincipalAutoencoder

The development version can be installed from github:

pip install -e "git+https://github.com/xwshen51/DistributionalPrincipalAutoencoder"

Usage Example

See this tutorial for an example on S-curve.

Contact information

If you meet any problems with the code, please submit an issue or contact Xinwei Shen.

Project details

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0.0.0.dev0 pre-release

Apr 20, 2024

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