Variational Inference for Deep Probabilistic Canonical Correlation Analysis

9 Mar 2020Mahdi KaramiDale Schuurmans

In this paper, we propose a deep probabilistic multi-view model that is composed of a linear multi-view layer based on probabilistic canonical correlation analysis (CCA) description in the latent space together with deep generative networks as observation models. The network is designed to decompose the variations of all views into a shared latent representation and a set of view-specific components where the shared latent representation is intended to describe the common underlying sources of variation among the views... (read more)

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