Equilibrium Modified K-Means Clustering Method
Pei Jian · Journal of Jilin University · 2006
In order to get the initial values of continuous hidden Markov models,a K-means clustering method that makes the clusters equably distributed in the space of training vectors is proposed.A punishment variable is introduced in the clustering,to limit too many vectors to congregate in one or several clusters.This makes the partition of the samples space more equably.Continuous hidden Markov model initial value experiments proved that the method reduced distortion distance,made the clusters distribute more equably,and improved the performance of vector quantization compared with the standard K-means clustering method and LBG(Linde Buzo Gray) clustering method.This method improved soundness of continuous hidden Markov model initial data in isolated-word recognition.It makes estimates of the parameters of Gauss probability functions approach the agonic estimates.