Semi-supervised learning in the presence of novel class instances

In this paper, we present an approach for learning in the semi-supervised setting in the presence of novel class instances. In this setting, data consists of a labeled portion and an unlabeled portion that contains novel class instances along with unlabeled known class instances. Novel class instances are instances from concepts that do not have labeled training examples. This setting is appropriate for the case in which data is abundant and labeling the entire data is prohibitively expensive. We provide a model and an inference framework that allow for a direct control over the portion of novel class instances in the unlabeled data. Experiments on synthetic data demonstrate the usefulness of the proposed approach. Comparison to state-of-the-art approaches for learning in the presence of novel class instances using unlabeled data illustrates the advantage in using the proposed method in term of accuracy.