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https://github.com/andleb/dpaefork

https://github.com/andleb/dpaefork

Science Score: 10.0%

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  • Owner: andleb
  • License: bsd-3-clause
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Fork of xwshen51/DistributionalPrincipalAutoencoder
Created almost 2 years ago · Last pushed almost 2 years ago

https://github.com/andleb/dpaeFork/blob/main/ 

# Distributional Principal Autoencoder

Distributional Principal Autoencoder (DPA) is a nonlinear dimension reduction method proposed in the paper "[*Distributional Principal Autoencoders*](https://arxiv.org/abs/2404.13649)" 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:
```sh
pip install DistributionalPrincipalAutoencoder
```

The development version can be installed from github:

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


## Usage Example

See [this tutorial](https://github.com/xwshen51/DistributionalPrincipalAutoencoder/blob/main/examples/scurve.ipynb) for an example on S-curve.


## Contact information
If you meet any problems with the code, please submit an issue or contact [Xinwei Shen](mailto:xinwei.shen@stat.math.ethz.ch).

Owner

  • Name: Andrej Leban
  • Login: andleb
  • Kind: user
  • Location: Ann Arbor, MI
  • Company: University of Michigan, Department of Statistics

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