Conjugate-prior-penalized learning of Gaussian mixture models for multifunction myoelectric hand control.
This paper presents a new learning method for Gaussian mixture models (GMMs) to improve their generalization ability. A traditional maximum a posterior (MAP) parameter estimate is used to achieve regularization based on conjugate priors. Plus, a model order selection criterion is derived from Bayesian-Laplace approaches, using the conjugate priors to measure the uncertainty of the estimated parameters. As a result, the proposed learning method avoids the possibility of convergence toward the boundary of the parameter space, and is also capable of selecting the optimal order for a GMM with more enhanced stability than conventional methods using a flat prior. When applying the proposed learning method to construct a GMM classifier for electromyogram (EMG) pattern recognition, the proposed GMM classifier achieves a high generalization ability and outperforms conventional classifiers in terms of recognition accuracy.
Chu
Jun-Uk
JU
School of Electrical Engineering and Computer Science, Kyungpook National University, Daegu 702-701, Korea. juchu@ee.knu.ac.kr
Lee
Yun-Jung
YJ
D000465:Algorithms; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D004576:Electromyography / Q000379:methods*; D006225:Hand / Q000502:physiology*; D006801:Humans; D008959:Models, Neurological; D015233:Models, Statistical; D009119:Muscle Contraction / Q000502:physiology*; D016011:Normal Distribution; D010363:Pattern Recognition, Automated / Q000379:methods*; D012371:Robotics / Q000379:methods*; D013813:Therapy, Computer-Assisted / Q000379:methods*; D014584:User-Computer Interface