  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
semi-supervised 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
inter-view 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
semi-supervised learning.
