Learning Class-Relevant Features and Class-Irrelevant Features via a Hybrid Third-Order RBM

Abstract

Restricted Boltzmann Machines are commonly used in unsupervised learning to extract features from training data. Since these features are learned for regenerating training data a classifier based on them has to be trained. If only a few of the learned features are discriminative other non-discriminative features will distract the classifier during the training process and thus waste computing resources for testing. In this paper, we present a hybrid third-order Restricted Boltzmann Machine in which class-relevant features (for recognizing) and class-irrelevant features (for generating only) are learned simultaneously. As the classification task uses only the class-relevant features, the test itself becomes very fast. We show that class-irrelevant features help class-relevant features to focus on the recognition task and introduce useful regularization effects to reduce the norms of class-relevant features. Thus there is no need to use weight-decay for the parameters of this model. Experiments on the MNIST, NORB and Caltech101 Silhouettes datasets show very promising results.