cca-zoo 2.3.9

Canonical Correlation Analysis Zoo: A collection of Regularized, Deep Learning based, Kernel, and Probabilistic methods in a scikit-learn style framework

CCA-Zoo

Unlock the hidden relationships in multiview data.

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
• Performance Highlights
• Detailed Documentation
• How to Cite
• Contribute
• Acknowledgments

🚀 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:

1. Star our GitHub repository.
2. 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