Francis Bach, Michael Jordan
We present an algorithm that induces a class of models with thin junction trees—models that are characterized by an upper bound on the size of the maximal cliques of their triangulated graph. By ensuring that the junction tree is thin, inference in our models remains tractable throughout the learning process. This allows both an efﬁcient implementation of an iterative scaling parameter estimation algorithm and also ensures that inference can be performed efﬁciently with the ﬁnal model. We illustrate the approach with applications in handwritten digit recognition and DNA splice site detection.