Flexible and Robust Bayesian Classification by Finite Mixture Models
نویسندگان
چکیده
The regularized Mahalanobis distance is proposed in the framework of finite mixture models to avoid commonly faced numerical difficulties encountered with EM. Its principle is applied to Gaussian and Student-t mixtures, resulting in reliable density estimates, the model complexity being kept low. Besides, the regularized models are robust to various noise types. Finally, it is shown that the quality of the associated Bayesian classification is near optimal on Ripley’s synthetic data set.
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تاریخ انتشار 2004