Key Laboratory of Information Processing and Intelligent Technology, Beijing University of Posts and Telecommunications, Beijing, China

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Abstract—EM (expectation-maximization) algorithm is a classical method for parameter estimation of HMM (Hidden Markov model). Concerning that EM algorithm is easily affected by initial parameter values, a mixture splitting algorithm based on decision boundary confusion(DBC) was proposed to describe more about boundary distribution. The algorithm mainly includes four aspects: firstly the number of incremented mixtures for every decision boundary could be determined according to decision boundary confusion; secondly the mixtures which are the closest to the decision boundary are chosen to split; thirdly the split mean of mixture is in the direction of decision boundary; finally the mixture number of a state is determined by the confusion between states. Our experiments show that our proposed algorithm is more effective for classification using HMM.

Index Terms—Mixture splitting, DBC, HMM, EM.

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Cite: Gang Liu, Wei Chen, Jun Guo, "A Research on Mixture Splitting for CHMM Based on DBC," Journal of Computers vol. 4, no. 11, pp. 1167-1174, 2009.