Scalable statistical learning: a modular Bayesian/Markov network approach

Authors:
Antonino Freno;Edmondo Trentin;Marco Gori
Affiliations:
Dipartimento di Ingegneria dell'Informazione, Universita degli Studi di Siena, Siena, Italy;Dipartimento di Ingegneria dell'Informazione, Universita degli Studi di Siena, Siena, Italy;Dipartimento di Ingegneria dell'Informazione, Universita degli Studi di Siena, Siena, Italy
Venue:
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Year:
2009

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Abstract

In this paper we propose a hybrid probabilistic graphical model for pseudo-likelihood estimation in high-dimensional domains. The model is based on Bayesian networks and Markov random fields. On the one hand, we prove that the proposed model is more expressive than Bayesian networks in terms of the representable distributions. On the other hand, we develop a computationally efficient structure learning algorithm, and we provide theoretical and experimental evidence showing how the modular nature of our model allows structure learning to scale up very well to high-dimensional datasets. The capability of the hybrid model to accurately learn complex networks of conditional independencies is illustrated by promising results in pattern recognition applications.