Semi-supervised Deep Representation Learning for Multi-View Problems

Abstract--While neural networks for learning representation of multi-view data have been previously proposed as one of the state-of-the-art multi-view dimension reduction techniques, how to make the representation discriminative with only a small amount of labeled data is not well-studied. We introduce a semisupervised neural network model, named Multi-view Discriminative Neural Network (MDNN), for multi-view problems. MDNN finds nonlinear view-specific mappings by projecting samples to a common feature space using multiple coupled deep networks. It is capable of leveraging both labeled and unlabeled data to project multi-view data so that samples from different classes are separated and those from the same class are clustered together. It also uses the interview correlation between views to exploit the available information in both the labeled and unlabeled data. Extensive experiments conducted on four datasets demonstrate the effectiveness of the proposed algorithm for multi-view semisupervised learning. In many real-world problems, more than one set of features, referred to as views of the data, are available. For example, a web page can be represented by text data, images, and metadata. Multiple views can help improve the performance of many learning tasks because each view can provide information complementary to others, and learning using all views can maximally exploit the information available.