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Learning general latent-variable probabilistic graphical models is a keytheoretical challenge in machine learning and artificial intelligence. Allprevious methods, including the EM algorithm and the spectral algorithms, facesevere limitations that largely restrict their applicability and affect theirperformance. In order to overcome these limitations, in this paper we introducea novel formulation of message-passing inference over junction trees namedpredictive belief propagation, and propose a new learning and inferencealgorithm for general latent-variable graphical models based on thisformulation. Our proposed algorithm reduces the hard parameter learning probleminto a sequence of supervised learning problems, and unifies the learning ofdifferent kinds of latent graphical models into a single learning framework,which is local-optima-free and statistically consistent. We then give a proofof the correctness of our algorithm and show in experiments on both syntheticand real datasets that our algorithm significantly outperforms both the EMalgorithm and the spectral algorithm while also being orders of magnitudefaster to compute.

Learning General Latent-Variable Graphical Models with Predictive Belief Propagation