A Componentwise Simulated Annealing EM Algorithm for Mixtures
نویسندگان
چکیده
This paper addresses the problem of fitting finite Gaussian Mixture Model (GMM) with unknown number of components to the univariate and multivariate data. The typical method for fitting a GMM is Expectation Maximization (EM) in which many challenges are involved i.e. how to initialize the GMM, how to restrict the covariance matrix of a component from becoming singular and setting the number of components in advance. This paper presents a simulated annealing EM algorithm along with a systematic initialization procedure by using the principals of stochastic exploration. The experiments have demonstrated the robustness of our approach on different datasets.
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