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Fully Distributed EM for Very Large Datasets
This video was recorded at 25th International Conference on Machine Learning (ICML), Helsinki 2008. In EM and related algorithms, E-step computations distribute easily, because data items are independent given parameters. For very large data sets, however, even storing all of the parameters in a single node for the M-step can be impractical. We present a framework which fully distributes the entire EM procedure. Each node interacts with only parameters relevant to its data, sending messages to other nodes along a junction-tree topology. We demonstrate improvements over a MapReduce approach, on two tasks: word alignment and topic modeling.
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This work is licensed under a Attribution-NonCommercial-NoDerivs 3.0 United States
