Markov random fields factorization with context-specific independencies

EDERA, ALEJANDRO; BROMBERG, FACUNDO; SCHLÜTER, FEDERICO

Washington DC

Conferencia; IEEE International Conference on Tools of Artificial Intelligence; 2013

IEEE

Learning the Markov network structure from data is a problem that has received considerable attention in machine learning, and in many other application fields. This work focuses on a particular approach for this purpose called Independence-Based learning. Such approach guarantees the learning of the correct structure efficiently, whenever data is sufficient for representing the underlying distribution. How- ever, an important issue of such approach is that the learned structures are encoded in an undirected graph. The problem with graphs is that they cannot encode some types of inde- pendence relations, such as the context-specific independences. They are a particular case of conditional independences that is true only for a certain assignment of its conditioning set, in contrast to conditional independences that must hold for all its assignments. In this work we present CSPC, an independence- based algorithm for learning structures that encode context- specific independences, and encoding them in a log-linear model instead of a graph. The central idea of CSPC is to combine the theoretical guarantees provided by the independence-based approach with the benefits of representing complex structures by using features in a log-linear model. We present experiments in a synthetic case, showing that CSPC is more accurate than the state-of-the-art Independence-Based algorithms when the underlying distribution contains CSIs.