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The mixture of factor analyzers(MFA) can accurately describe high resolution range profile(HRRP) statistical characteristics.But how to determine the proper number of the models is a problem.This paper develops a variational Bayesian mixture of factor analyzers(VBMFA) model.This procedure can obtain a lower bound on the Bayesian integral using the Jensen’s inequality. An analytical solution of the Bayesian integral could be obtained by a hypothesis that latent variables in the model are independent.During computing the parameters of the model,birth-death moves are utilized to determine the optimal number of model automatically.Experimental results for measured data show that the VBMFA method has better recognition performance than FA and MFA method.
The mixture of factor analyzers (MFA) can accurately describe high resolution range profile (HRRP) statistical characteristics. But how to determine the proper number of the models is a problem. This paper develops a variational Bayesian mixture of factor analyzers (VBMFA) model. This procedure can obtain a lower bound on the Bayesian integral using the Jensen’s inequality. An analytical solution of the Bayesian integral could be obtained by a hypothesis that latent variables in the model are independent.During computing the parameters of the model, birth-death moves are utilized to determine the optimal number of model automatically. Experimental results for measured data show that the VBMFA method has better recognition performance than FA and MFA method.