Variational Inference for Deep Probabilistic Canonical Correlation Analysis
This work addresses multi-view learning for researchers in machine learning, offering an incremental improvement by extending deep variational inference to this domain.
The authors tackled the problem of multi-view learning by proposing a deep probabilistic model that combines probabilistic canonical correlation analysis (CCA) with deep generative networks, achieving efficient variational inference and successful integration of relationships between multiple views.
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. An efficient variational inference procedure is developed that approximates the posterior distributions of the latent probabilistic multi-view layer while taking into account the solution of probabilistic CCA. A generalization to models with arbitrary number of views is also proposed. The empirical studies confirm that the proposed deep generative multi-view model can successfully extend deep variational inference to multi-view learning while it efficiently integrates the relationship between multiple views to alleviate the difficulty of learning.