Skip to content

Latest commit

 

History

History
27 lines (16 loc) · 876 Bytes

README.md

File metadata and controls

27 lines (16 loc) · 876 Bytes
Raw

sparsecca

Python implementations for Sparse CCA algorithms. Includes:

  • Sparse (multiple) CCA based on Penalized Matrix Decomposition (PMD) from Witten et al, 2009.
  • Sparse CCA based on Iterative Penalized Least Squares from Mai et al, 2019.

One main difference between these two is that while the first is very simple it assumes datasets to be white.

Installation

Dependencies

In addition to basic scientific packages such as numpy and scipy, iterative penalized least squares needs either glmnet_python or pyglmnet to be installed.

This package can be installed normally with

git clone https://github.com/Teekuningas/sparsecca.git
cd sparsecca
python setup.py install

Usage

See examples, https://teekuningas.github.io/sparsecca

Acknowledgements

Great thanks to the original authors, see Witten et al, 2009 and Mai et al, 2019.