Adaptive parameter estimation of GMM and its application in clustering

摘要

Parameter estimation of Gaussian mixture model (GMM) has begun to gain attention in the field of science and engineering, and it has gradually become one of the most popular research topics. In particular, the rapid development of theoretical progress on globally optimal convergence promotes the widespread application of Gaussian mixtures in data clustering. So this paper introduces a novel parameter estimation algorithm called TDAVBEM, which combines the Tsallis entropy and a deterministic annealing (DA) algorithm on the basis of the variational bayesian expected maximum (VBEM) to simultaneously implement the parameter estimation and select the optimal components of GMM. We experimentally certified the effectiveness and robustness of our proposed algorithm passes through comparing it with several parameter evaluation methods and its application in data clustering.