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.
In addition to basic scientific packages such as numpy and scipy, iterative penalized least squares needs either glmnet_python or pyglmnet to be installed.
git clone https://github.com/Teekuningas/sparsecca.git
cd sparsecca
python setup.py install
See examples, https://teekuningas.github.io/sparsecca
Great thanks to the original authors, see Witten et al, 2009 and Mai et al, 2019.