Accelerating EM: an empirical study
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Update rules for parameter estimation in Bayesian networks
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
On the convergence of bound optimization algorithms
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Data Mining and Knowledge Discovery
Knowledge and Information Systems
Statistical estimation of delays in a multicast tree using accelerated EM
Queueing Systems: Theory and Applications
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This paper presents the triple jump framework for accelerating the EM algorithm and other bound optimization methods. The idea is to extrapolate the third search point based on the previous two search points found by regular EM. As the convergence rate of regular EM becomes slower, the distance of the triple jump will be longer, and thus provide higher speedup for data sets where EM converges slowly. Experimental results show that the triple jump framework significantly outperforms EM and other acceleration methods of EM for a variety of probabilistic models, especially when the data set is sparse. The results also show that the triple jump framework is particularly effective for Cluster Models.