cca-zoo 1.10.dev1

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

Installation

Note: for standard install use: pip install cca-zoo

For deep learning elements use: pip install cca-zoo[deep]

For probabilistic elements use: pip install cca-zoo[probabilistic]

This means that there is no need to install the large pytorch package or numpyro to run cca-zoo unless you wish to use deep learning

Documentation

Available at https://cca-zoo.readthedocs.io/en/latest/

Citation:

If this repository was helpful to you please do give a star.

In case this work is used as part of research I attach a DOI bibtex entry:

@software{james_chapman_2021_4925892,
  author       = {James Chapman and
                  Hao-Ting Wang},
  title        = {jameschapman19/cca\_zoo:},
  month        = jun,
  year         = 2021,
  publisher    = {Zenodo},
  version      = {v1.6.1},
  doi          = {10.5281/zenodo.4925892},
  url          = {https://doi.org/10.5281/zenodo.4925892}
}

Implemented Methods

Standard Install

CCA (Canonical Correlation Analysis)

Solutions based on either alternating least squares or as the solution to genrralized eigenvalue problem

PLS (Partial Least Squares)

rCCA (Ridge Regularized Canonical Correlation Analysis)

https://www.sciencedirect.com/science/article/abs/pii/0304407676900105?via%3Dihub

GCCA (Generalized CCA) :

https://academic.oup.com/biomet/article-abstract/58/3/433/233349?redirectedFrom=fulltext

MCCA (Multiset CCA) :

K(M)CCA (kernel Multiset CCA) :

TCCA (Tensor CCA) :

https://arxiv.org/pdf/1502.02330.pdf

KTCCA (kernel Tensor CCA) :

https://arxiv.org/pdf/1502.02330.pdf

SCCA (Sparse CCA) :

https://onlinelibrary.wiley.com/doi/abs/10.1111/biom.13043

SPLS (Sparse PLS/Penalized Matrix Decomposition) :

https://web.stanford.edu/~hastie/Papers/PMD_Witten.pdf

ElasticCCA (Penalized CCA) :

https://pubmed.ncbi.nlm.nih.gov/19689958/

SWCCA (Sparse Weighted CCA) :

https://arxiv.org/abs/1710.04792v1#:~:text=However%2C%20classical%20and%20sparse%20CCA%20models%20consider%20the,where%20weights%20are%20used%20for%20regularizing%20different%20samples.

SpanCCA

http://akyrillidis.github.io/pubs/Conferences/cca.pdf

Deep Install

DCCA (Deep CCA) :

https://ttic.uchicago.edu/~klivescu/papers/andrew_icml2013.pdf https://arxiv.org/pdf/1510.02054v1.pdf Using either Andrew's original Tracenorm Objective or Wang's alternating least squares solution

DGCCA (Deep Generalized CCA) :

https://www.aclweb.org/anthology/W19-4301.pdf An alternative objective based on the linear GCCA solution. Can be extended to more than 2 views

DMCCA (Deep Multiset CCA) :

https://arxiv.org/abs/1904.01775 An alternative objective based on the linear MCCA solution. Can be extended to more than 2 views

DTCCA (Deep Tensor CCA) :

https://arxiv.org/pdf/2005.11914.pdf

DCCAE (Deep Canonically Correlated Autoencoders) :

http://proceedings.mlr.press/v37/wangb15.pdf

DVCCA/DVCCA Private (Deep variational CCA):

https://arxiv.org/pdf/1610.03454.pdf

Probabilistic Install

Variational Bayes CCA

https://ieeexplore.ieee.org/document/4182407

Contributions

A guide to contributions is available at https://cca-zoo.readthedocs.io/en/latest/developer_info/contribute.html

Sources

I've added this section to give due credit to the repositories that helped me in addition to their copyright notices in the code where relevant.

Models can be tested on data from MNIST datasets provided by the torch package (https://pytorch.org/) and the UCI dataset provided by mvlearn package (https://mvlearn.github.io/)

Other Implementations of (regularised)CCA/PLS:

MATLAB implementation https://github.com/anaston/PLS_CCA_framework

Implementation of Sparse PLS:

MATLAB implementation of SPLS by @jmmonteiro (https://github.com/jmmonteiro/spls)

Other Implementations of DCCA/DCCAE:

Keras implementation of DCCA from @VahidooX's github page(https://github.com/VahidooX) The following are the other implementations of DCCA in MATLAB and C++. These codes are written by the authors of the original paper:

Torch implementation of DCCA from @MichaelVll & @Arminarj: https://github.com/Michaelvll/DeepCCA

C++ implementation of DCCA from Galen Andrew's website (https://homes.cs.washington.edu/~galen/)

MATLAB implementation of DCCA/DCCAE from Weiran Wang's website (http://ttic.uchicago.edu/~wwang5/dccae.html)

MATLAB implementation of TCCA from https://github.com/rciszek/mdr_tcca

Implementation of VAE:

Torch implementation of VAE (https://github.com/pytorch/examples/tree/master/vae)