sppcax

Sparse Probabilistic Principal Component Analysis with Bayesian Model Reduction written in Jax

sppcax is a Python implementation of sparse probabilistic principal component analysis (SPPCA) using Bayesian model reduction and coordinate ascent variational inference. This method provides an efficient way to perform dimensionality reduction while automatically determining the optimal number of components and encouraging sparsity in the loading matrix.

Installation

You can install sppcax for development with:

git clone https://github.com/dimarkov/sppcax.git
cd sppcax
pip install -e .

Note

This project has been set up using PyScaffold 4.5. For details and usage information on PyScaffold see https://pyscaffold.org/.