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• W2152565110 abstract "There are two open problems when finite mixture densities are used to model multivariate data: the selection of the number of components and the initialization. In this paper, we propose an online (recursive) algorithm that estimates the parameters of the mixture and that simultaneously selects the number of components. The new algorithm starts with a large number of randomly initialized components. A prior is used as a bias for maximally structured models. A stochastic approximation recursive learning algorithm is proposed to search for the maximum a posteriori (MAP) solution and to discard the irrelevant components." @default.
• W2152565110 created "2016-06-24" @default.
• W2152565110 creator A5065298754 @default.
• W2152565110 creator A5067264401 @default.
• W2152565110 date "2004-05-01" @default.
• W2152565110 modified "2023-09-27" @default.
• W2152565110 title "Recursive unsupervised learning of finite mixture models" @default.
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• W2152565110 doi "https://doi.org/10.1109/tpami.2004.1273970" @default.
• W2152565110 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/15460286" @default.
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