BYY harmony learning of log-normal mixtures with automated model selection

Bayesian Ying–Yang (BYY) harmony learning system is a powerful tool for statistical learning. Via the BYY harmony leaning of finite mixtures, model selection, i.e., the selection of an appropriate number of components for the mixture, can be made automatically during parameter learning on a given dataset. In this paper, an adaptive gradient BYY harmony learning algorithm is proposed for log-normal mixtures to implement parameter learning with automated model selection. It is demonstrated by the experiments on both synthetic and real-world datasets that the proposed BYY harmony learning algorithm not only has the ability of automated model selection, but also leads to a rather good estimation of the parameters in the original log-normal mixture.