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An effective strategy for initializing the EM algorithm in finite mixture models

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  • Semhar Michael

    (South Dakota State University)

  • Volodymyr Melnykov

    (University of Alabama)

Abstract

Finite mixture models represent one of the most popular tools for modeling heterogeneous data. The traditional approach for parameter estimation is based on maximizing the likelihood function. Direct optimization is often troublesome due to the complex likelihood structure. The expectation–maximization algorithm proves to be an effective remedy that alleviates this issue. The solution obtained by this procedure is entirely driven by the choice of starting parameter values. This highlights the importance of an effective initialization strategy. Despite efforts undertaken in this area, there is no uniform winner found and practitioners tend to ignore the issue, often finding misleading or erroneous results. In this paper, we propose a simple yet effective tool for initializing the expectation–maximization algorithm in the mixture modeling setting. The idea is based on model averaging and proves to be efficient in detecting correct solutions even in those cases when competitors perform poorly. The utility of the proposed methodology is shown through comprehensive simulation study and applied to a well-known classification dataset with good results.

Suggested Citation

  • Semhar Michael & Volodymyr Melnykov, 2016. "An effective strategy for initializing the EM algorithm in finite mixture models," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 10(4), pages 563-583, December.
    • Handle: RePEc:spr:advdac:v:10:y:2016:i:4:d:10.1007_s11634-016-0264-8
    DOI: 10.1007/s11634-016-0264-8
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    References listed on IDEAS

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    2. Prates, Marcos Oliveira & Lachos, Victor Hugo & Barbosa Cabral, Celso Rômulo, 2013. "mixsmsn: Fitting Finite Mixture of Scale Mixture of Skew-Normal Distributions," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 54(i12).
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    4. Lebret, Rémi & Iovleff, Serge & Langrognet, Florent & Biernacki, Christophe & Celeux, Gilles & Govaert, Gérard, 2015. "Rmixmod: The R Package of the Model-Based Unsupervised, Supervised, and Semi-Supervised Classification Mixmod Library," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 67(i06).
    5. Melnykov, Volodymyr & Melnykov, Igor, 2012. "Initializing the EM algorithm in Gaussian mixture models with an unknown number of components," Computational Statistics & Data Analysis, Elsevier, vol. 56(6), pages 1381-1395.
    6. Christian Hennig, 2010. "Methods for merging Gaussian mixture components," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 4(1), pages 3-34, April.
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    Cited by:

    1. Semhar Michael & Tatjana Miljkovic & Volodymyr Melnykov, 2020. "Mixture modeling of data with multiple partial right-censoring levels," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 14(2), pages 355-378, June.
    2. Salvatore D. Tomarchio & Paul D. McNicholas & Antonio Punzo, 2021. "Matrix Normal Cluster-Weighted Models," Journal of Classification, Springer;The Classification Society, vol. 38(3), pages 556-575, October.

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