A Novel Method of Initializing the Expectation Maximization Algorithm Based on the Independent Mixture Model

The state of the Hidden Markov Tree (HMT) model can not be observed, only the variable associated with the state is observed. So parameter estimation of the HMT model through observed variable is an incomplete data estimation problem. The Expectation Maximization (EM) algorithm, which can solve the maximum likelihood parameter estimation, plays an important role in dealing with the parameter estimation of incomplete data. It also has been widely applied to estimate the parameters of the HMT model. Especially, when initial parameters deviate from the true value greatly, iteration number of the EM algorithm is large and convergence rate becomes slow. Such problems can be solved after using a lower computation initialization method. In this paper, an initialization method based on the Independent Mixture (IM) model is proposed and the implementation of this method is analyzed in detail. After using the IM model to initialize the parameters of the EM algorithm, the iteration number is significantly reduced and the convergence rate is greatly improved when using the EM algorithm to estimate the parameters of the HMT model. Finally, computer simulation is employed to validate the feasibility and availability of this method.