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• W2897788939 abstract "We study the convergence behavior of the Expectation Maximization (EM) algorithm on Gaussian mixture models with an arbitrary number of mixture components and mixing weights. We show that as long as the means of the components are separated by at least $Omega(sqrt{min{M,d}})$, where $M$ is the number of components and $d$ is the dimension, the EM algorithm converges locally to the global optimum of the log-likelihood. Further, we show that the convergence rate is linear and characterize the size of the basin of attraction to the global optimum." @default.
• W2897788939 created "2018-10-26" @default.
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• W2897788939 date "2018-10-09" @default.
• W2897788939 modified "2023-09-24" @default.
• W2897788939 title "Statistical Convergence of the EM Algorithm on Gaussian Mixture Models" @default.
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• W2897788939 doi "https://doi.org/10.48550/arxiv.1810.04090" @default.
• W2897788939 hasPublicationYear "2018" @default.
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