Title: Learning General Latent-Variable Graphical Models with Predictive Belief Propagation and Hilbert Space Embeddings,
Abstract: In this paper, we propose a new algorithm for learning general
latent-variable probabilistic graphical models using the techniques of
predictive state representation, instrumental variable regression, and
reproducing-kernel Hilbert space embeddings of distributions. Under this new
learning framework, we first convert latent-variable graphical models into
corresponding latent-variable junction trees, and then reduce the hard
parameter learning problem into a pipeline of supervised learning problems,
whose results will then be used to perform predictive belief propagation over
the latent junction tree during the actual inference procedure. We then give
proofs of our algorithm's correctness, and demonstrate its good performance in
experiments on one synthetic dataset and two real-world tasks from
computational biology and computer vision - classifying DNA splice junctions
and recognizing human actions in videos.