Deep Multi-view Information Bottleneck

In many classification problems, the predictions can be enhanced by fusing information from different data views. In particular, when the information from different views complement each other, it is expected that multi-view learning will lead to improved predictive performance. In this paper, we proposed a supervised multi-view learning framework based on the information bottleneck principle to filter out irrelevant and noisy information from multiple views and learn an accurate joint representation. Specifically, our proposed method maximizes the mutual information between the labels and the learned joint representation while minimizing the mutual information between the learned latent representation of each view and the original data representation. As the relationships between different views are often complicated and nonlinear, we employed deep neural networks to learn the latent representation and to disentangle their complex dependencies. However, since the computation of mutual information can be intractable, we employed the variational inference method to efficiently solve the optimization problem. We performed extensive experiments on various synthetic and real-world datasets to demonstrate the effectiveness of the framework.