Using the H-Divergence to Prune Probabilistic Automata

A problem usually encountered in probabilistic automata learning is the difficulty to deal with large training samples and/or wide alphabets. This is partially due to the size of the resulting Probabilistic Prefix Tree (PPT) from which state merging-based learning algorithms are generally applied. In this paper, we propose a novel method to prune PPTs by making use of the H-divergence d_H, recently introduced in the field of domain adaptation. d_H is based on the classification error made by an hypothesis learned from unlabeled examples drawn according to two distributions to compare. Through a thorough comparison with state-of-the-art divergence measures, we provide experimental evidences that demonstrate the efficiency of our method based on this simple and intuitive criterion.