Variational learning of finite Beta-Liouville mixture models using component splitting

Abstract

Recently, finite Beta-Liouville mixture models have proved to be an effective and powerful knowledge representation and inference engine in several machine learning and data mining applications. In this paper, we propose a component splitting and local model selection method to address the problem of learning and selecting finite Beta-Liouville mixture models in an incremental variational way. Within the proposed principled variational learning framework, all the involved parameters and model complexity (i.e. the number of mixture components) can be estimated simultaneously in a closed-form. We demonstrate the effectiveness of the proposed approach through both synthetic data as well as two challenging real-world applications namely human activities modeling and recognition, and facial expressions recognition.