Initializing the EM algorithm in Gaussian mixture models with an unknown number of components

Volodymyr Melnykov and Igor Melnykov

Computational Statistics & Data Analysis, 2012, vol. 56, issue 6, 1381-1395

Abstract: An approach is proposed for initializing the expectation–maximization (EM) algorithm in multivariate Gaussian mixture models with an unknown number of components. As the EM algorithm is often sensitive to the choice of the initial parameter vector, efficient initialization is an important preliminary process for the future convergence of the algorithm to the best local maximum of the likelihood function. We propose a strategy initializing mean vectors by choosing points with higher concentrations of neighbors and using a truncated normal distribution for the preliminary estimation of dispersion matrices. The suggested approach is illustrated on examples and compared with several other initialization methods.

Keywords: Gaussian mixture model; Initialization; EM algorithm; Eigenvalue decomposition; Truncated normal distribution (search for similar items in EconPapers)
Date: 2012
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Citations: View citations in EconPapers (17)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:56:y:2012:i:6:p:1381-1395

DOI: 10.1016/j.csda.2011.11.002

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