Canonical Correlation Analysis Zoo: A collection of Regularized, Deep Learning based, Kernel, and Probabilistic methods in a scikit-learn style framework
Project description
In an era inundated with data, unraveling the intricate connections between different data views is paramount. CCA-Zoo emerges as the definitive toolkit for this, offering an extensive suite of linear, kernel, and deep methods for canonical correlation analysis.
Inspired by the simplicity and effectiveness of scikit-learn
and mvlearn
, CCA-Zoo offers a seamless experience with its fit
/transform
/fit_transform
methods.
Table of Contents
🚀 Get Started in 5 Minutes
Installation
Whether you're a pip
enthusiast or a poetry
lover, we've got you covered:
pip install cca-zoo
# or for the adventurous
pip install cca-zoo[probabilistic]
Poetry aficionados can dive in with:
poetry add cca-zoo
# or with a twist of probability
poetry add cca-zoo[probabilistic]
📚 Dive Deep
Embark on a journey through multiview correlations with our comprehensive guide.
🙏 Show Your Love
CCA-Zoo thrives on the support of its community. If it's made a splash in your research, consider sprinkling some stardust by citing our JOSS paper or simply starring our repo. Every gesture counts!
📜 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!
🙌 Props
A nod to the stalwarts and pioneers whose work paved the way. Dive into their implementations and explorations:
- Regularised CCA/PLS: MATLAB
- Sparse PLS: MATLAB SPLS
- DCCA/DCCAE: Keras DCCA, Torch DCCA, and more...
- VAE: Torch VAE
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