Automatic Feature Learning and Parameter Estimation for Hidden Markov Models Using MCE and Gibbs Sampling
Author: Xuping Zhang
Publisher:
Published: 2009
Total Pages:
ISBN-13:
DOWNLOAD EBOOKSince our model is based on gradient decent methods, the MCE method cannot guarantee a global optimal solution and is very sensitive to initialization. We propose a new learning method based on Gibbs sampling to learn the parameters. The new learning method samples parameters from their individual conditional probability distribution instead to maximize the probability directly. This new method is more robust to initialization, and can generally find a better solution. We also developed a new learning method based on Gibbs sampling to learn parameters for continuous hidden Markov models with multivariate Gaussian mixtures. Because hidden Markov models with multivariate Gaussian mixtures are commonly used HMM models in applications, we propose a learning method based on Gibbs sampling. The proposed method is empirically shown to be more robust than comparable expectation-maximization algorithms. We performed experiments using both synthetic and real data. The results show that both methods work better than the standard HMM methods used in landmine detection applications. Experiments with handwritten digits are also presented. The results show that the HMM-model framework with the automatic learning feature algorithm again performed better than the same framework with the man-made feature.
