Variational Interpretable Learning from Multi-view Data
This work addresses multi-view learning for domains requiring interpretable representations, though it appears incremental as it extends existing CCA methods with deep generative networks.
The authors tackled the problem of learning from multi-view data by proposing DICCA, a deep interpretable variational canonical correlation analysis model that disentangles shared and view-specific variations, achieving competitive performance across real-world datasets.
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.