Evolving multivariate mixture density estimates for classification
Donald E. Waagen, M. D. Parsons, John R. McDonnell, Jeffrey D. Argast · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1994
Finite mixture models (mixture models) estimate probability density functions based on a weighted combination of density functions. This work investigates a combined stochastic and deterministic optimization approach of a generalized kernel function for multivariate mixture density estimation. Mixture models are selected and optimized by combining the optimization characteristics of a multi-agent stochastic optimization algorithm, based on evolutionary programming, and the EM algorithm. A classification problem is approached by optimizing a mixture density estimate for each class. Rissanen's minimum description length criterion provides the selection mechanism for evaluating mixture models. A comparison of each class' posterior probability (Bayes rule) provides the classification decision procedure. A 2-D, two- class classification problem is posed, and classification performance of the optimal mixture models is compared with a kernel estimator whose bandwidth is optimized using the technique of least-squares cross-validation.