  The main idea of canonical correlation analysis (CCA) is to map different
views onto a common latent space with maximum correlation. We propose a deep
interpretable variational canonical correlation analysis (DICCA) for multi-view
learning. The developed model extends the existing latent variable model for
linear CCA to nonlinear models through the use of deep generative networks.
DICCA is designed to disentangle both the shared and view-specific variations
for multi-view data. To further make the model more interpretable, we place a
sparsity-inducing prior on the latent weight with a structured variational
autoencoder that is comprised of view-specific generators. Empirical results on
real-world datasets show that our methods are competitive across domains.

Machine Learning;Machine Learning