BYY harmony learning on finite mixture: Adaptive gradient implementation and a floating RPCL mechanism

In tackling the learning problem on a set of finite samples, Bayesian Ying-Yang (BYY) harmony learning has developed a new learning mechanism that makes model selection implemented either automatically during parameter learning or in help of evaluating a new class of model selection criteria. In this paper, parameter learning with automated model selection has been studied for finite mixture model via an adaptive gradient learning algorithm for BYY harmony learning on a specific bidirectional architecture (BI-architecture). Via theoretical analysis, it has shown that the adaptive gradient learning implements a mechanism of floating rival penalized competitive learning (RPCL) among the components in the mixture. Also, the simulation results are demonstrated well for the adaptive gradient algorithm on the sample data sets from Gaussian mixtures with certain degree of overlap. Moreover, the adaptive gradient algorithm is applied to classification of the Iris data and unsupervised color image segmentation.