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• W2230229135 abstract "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." @default.
• W2230229135 created "2016-06-24" @default.
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• W2230229135 date "2015-01-01" @default.
• W2230229135 modified "2023-09-24" @default.
• W2230229135 title "A Componentwise Simulated Annealing EM Algorithm for Mixtures" @default.
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• W2230229135 doi "https://doi.org/10.1007/978-3-319-24489-1_25" @default.
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