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Linear and Parallel Learning of Markov Random Fields
Authors: Yariv Mizrahi, Misha Denil and Nando D. Freitas
Conference: Proceedings of the 31st International Conference on Machine Learning (ICML-14)
Abstract: We introduce a new embarrassingly parallel parameter learning algorithm for Markov random fields which is efficient for a large class of practical models. Our algorithm parallelizes naturally over cliques and, for graphs of bounded degree, its complexity is linear in the number of cliques. Unlike its competitors, our algorithm is fully parallel and for log-linear models it is also data efficient, requiring only the local sufficient statistics of the data to estimate parameters.
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