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Canonical Correlation Analysis Methods: cca-zoo

CCA, GCCA (Generalized CCA), MCCA (Multiset CCA), DCCA (Deep CCA), DGCCA (Deep Generalized CCA), DMCCA (Deep Multiset CCA), DVCCA (Deep Variational CCA), DCCAE (Deep Canonically Correlated Autoencoders), KCCA (Kernel CCA) and regularised variants using alternating least squares

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

Can be installed using pip install cca-zoo

Recently added DCCA by non-linear orthogonal iterations (https://arxiv.org/pdf/1510.02054.pdf)

This is a compilation of methods for: -CCA (various implementations) -RCCA (ridge penalty) -Sparse CCA (Witten/Parkhomenko,Waaijenborg) -Kernel CCA (e.g. Hardoon) -Deep CCA (Andrew) and DCCA by non-linear orthogonal iterations (Wang) -Deep Canonically Correlated Autoencoders (Wang) -Deep Variational CCA and Deep Variational CCA_private (Wang)

All models are evaluated on a noisy version of MNIST dataset with thanks to @VahidooX, @MichaelVll @Arminarj. The train/validation/test split is the original split of MNIST.

You can download them from noisymnist_view1.gz and noisymnist_view2.gz

The DCCAE is just a DCCA with reconstruction networks from the latent space and additional reconstruction losses. As such I implement both DCCA and DCCAE in a single model with a parameter weighting the reconstruction loss (i.e. 0 in the DCCA case)

The DVCCA (in layman's terms!) can be understood as an encoder-decoder with 3 loss terms: one that ensures a good encoding (this is the K-L divergence between the encoding and a N(0,1) gaussian), and two ensuring good reconstructions from the latent variable to the original views.

If you are familiar with the variational autoencoder, you will notice that 2 out of 3 of these losses are the same as the VAE set up with an additional reconstruction terms for the second view.

The DVCCA_private extends the DVCCA by adding private latent variables. This gives us 2 additional loss terms ensuring a 'good' encoding of the private information from each view (the KL divergence between the encoding and a N(0,1) gaussian).

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)

Implementation of VAE:

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

Implementation of Sparse PLS:

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

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