Asymmetric mixture model with variational Bayesian learning
Bayesian detection for the symmetric Gaussian mixture model has recently received great attention for pattern recognition problems. However, in many applications, the distribution of the data has a non-Gaussian and non-symmetric form. This study presents a new asymmetric mixture model for model detection. In this paper, the proposed asymmetric distribution is modeled with multiple Student's-t distributions, which are heavily tailed and more robust than Gaussian distributions. Our method has the flexibility to fit different shapes of observed data such as non-Gaussian and non-symmetric. Another advantage is that the proposed algorithm, which is based on the variational Bayesian learning, can simultaneously optimize over the number of the Student's-t distribution that is used to model each asymmetric distribution, and the number of components. The performance of the proposed model is compared to other mixture models, demonstrating the robustness, accuracy, and effectiveness of our method.