Efficient Training for Positive Unlabeled Learning
This addresses the scalability issue for practitioners dealing with large unlabeled datasets in PU learning, though it is incremental as it builds on existing theoretical frameworks.
The paper tackles the scalability problem in positive unlabeled (PU) learning by proposing a novel algorithm that is theoretically proven to be optimal, with experimental results showing superior computational and memory performance.
Positive unlabeled (PU) learning is useful in various practical situations, where there is a need to learn a classifier for a class of interest from an unlabeled data set, which may contain anomalies as well as samples from unknown classes. The learning task can be formulated as an optimization problem under the framework of statistical learning theory. Recent studies have theoretically analyzed its properties and generalization performance, nevertheless, little effort has been made to consider the problem of scalability, especially when large sets of unlabeled data are available. In this work we propose a novel scalable PU learning algorithm that is theoretically proven to provide the optimal solution, while showing superior computational and memory performance. Experimental evaluation confirms the theoretical evidence and shows that the proposed method can be successfully applied to a large variety of real-world problems involving PU learning.