cca_zoo.deep: Deep Methods

The cca_zoo.deep module includes a variety of deep CCA algorithms.

Models

deep.DCCA(latent_dimensions[, encoders])	A class used to fit a DCCA model.
deep.DCCA(latent_dimensions[, encoders])	A class used to fit a DCCA model.
deep.DCCA_GHA(latent_dimensions[, encoders, eps])	References
deep.DCCA_SVD(latent_dimensions[, encoders, eps])	References
deep.DGCCA(latent_dimensions[, encoders, eps])	A class used to fit a DGCCA model.
deep.DCCAE(latent_dimensions[, objective, ...])	A class used to fit a DCCAE model.
deep.DCCA_NOI(latent_dimensions[, encoders, ...])	A class used to fit a DCCA model by non-linear orthogonal iterations
deep.DCCA_SDL(latent_dimensions[, encoders, ...])	A class used to fit a Deep _CCALoss by Stochastic Decorrelation model.
deep.DVCCA(latent_dimensions[, encoders, ...])	A class used to fit a DVCCA model.
deep.BarlowTwins(latent_dimensions[, ...])	A class used to fit a Barlow Twins model.
deep.DTCCA(latent_dimensions[, encoders, eps])	A class used to fit a DTCCA model.
deep.DCCA_EY(latent_dimensions[, encoders, eps])	References
deep.SplitAE(latent_dimensions[, encoder, ...])	A class used to fit a Split Autoencoder model.