Learning with Hidden Information Using a Max-Margin Latent Variable Model

Classifier learning is challenging when the training data is inadequate in either quantity or quality. Prior knowledge hence is important in such cases to improve the performance of classification. In this paper we study a specific type of prior knowledge called hidden information, which is only available during training but not available during testing. Hidden information has abundant applications in many areas but has not been thoroughly studied. In this paper, we propose to exploit the hidden information during training to help design an improved classifier. Towards this goal, we introduce a novel approach which automatically learns and transfers the useful hidden information through a latent variable model. Experiments on both digit recognition and gesture recognition tasks demonstrate the effectiveness of the proposed method in capturing hidden information for improved classification.