Deep Variational Canonical Correlation Analysis

Understand

We present deep variational canonical correlation analysis (VCCA), a deep multi-view learning model that extends the latent variable model interpretation of linear CCA to nonlinear observation models parameterized by deep neural networks.

• We derive variational lower bounds of the data likelihood by parameterizing the posterior probability of the latent variables from the view that is available at test time.
• We also propose a variant of VCCA called VCCA-private that can, in addition to the "common variables" underlying both views, extract the "private variables" within each view, and disentangles the shared and private information for multi-view data without hard supervision.
• Experimental results on real-world datasets show that our methods are competitive across domains.