Canonical Correlation Analysis Zoo: A collection of Regularized, Deep Learning based, Kernel, and Probabilistic methods in a scikit-learn style framework
Project description
Introduction
In today's data-driven world, revealing hidden relationships across multiview datasets is critical. CCA-Zoo is your go-to library, featuring a robust selection of linear, kernel, and deep canonical correlation analysis methods.
Designed to be user-friendly, CCA-Zoo is inspired by the ease of use in scikit-learn
and mvlearn
. It provides a seamless programming experience with familiar fit
, transform
, and fit_transform
methods.
📖 Table of Contents
🚀 Quick Start
Installation
Whether you're a pip
enthusiast or a poetry
aficionado, installing CCA-Zoo is a breeze:
pip install cca-zoo
# For additional features
pip install cca-zoo[probabilistic]
For Poetry users:
poetry add cca-zoo
# For extra features
poetry add cca-zoo[probabilistic]
🏎️ Performance Highlights
CCA-Zoo shines when it comes to high-dimensional data analysis. It significantly outperforms scikit-learn, particularly as dimensionality increases. For comprehensive benchmarks, see our script and the graph below.
📚 Detailed Documentation
Embark on a journey through multiview correlations with our comprehensive guide.
🙏 How to Cite
Your support means a lot to us! If CCA-Zoo has been beneficial for your research, there are two ways to show your appreciation:
- Star our GitHub repository.
- Cite our research paper in your publications.
For citing our work, please use the following BibTeX entry:
@software{Chapman_CCA-Zoo_2023,
author = {Chapman, James and Wang, Hao-Ting and Wells, Lennie and Wiesner, Johannes},
doi = {10.5281/zenodo.4382739},
month = aug,
title = {{CCA-Zoo}},
url = {https://github.com/jameschapman19/cca_zoo},
version = {2.3.0},
year = {2023}
}
Or check out our JOSS paper:
📜 Chapman et al., (2021). CCA-Zoo: A collection of Regularized, Deep Learning based, Kernel, and Probabilistic CCA methods in a scikit-learn style framework. Journal of Open Source Software, 6(68), 3823, Link.
👩💻 Contribute
Every idea, every line of code adds value. Check out our contribution guide and help CCA-Zoo soar to new heights!
🙌 Acknowledgments
Special thanks to the pioneers whose work has shaped this field. Explore their work:
- Regularised CCA/PLS: MATLAB
- Sparse PLS: MATLAB SPLS
- DCCA/DCCAE: Keras DCCA, Torch DCCA
- VAE: Torch VAE
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.