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Intuitionistic Fuzziness and Other Intelligent Theories and Their Applications pp 29–49Cite as

Mixture Initialization Based on Prior Data Visual Analysis

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Part of the book series: Studies in Computational Intelligence ((SCI,volume 757))

Abstract

The initialization is known to be a critical task for running a mixture estimation algorithm. A majority of approaches existing in the literature are related to initialization of the expectation-maximization algorithm widely used in this area. This study focuses on the initialization of the recursive mixture estimation for the case of normal components, where the mentioned methods are not applicable. Its key part is a choice of the initial statistics of normal components. Several initialization techniques based on visual analysis of prior data are discussed. Validation experiments are presented.

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Acknowledgements

The research was supported by project GAČR GA15-03564S.

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Authors and Affiliations

  1. Department of Signal Processing, The Czech Academy of Sciences, Institute of Information Theory and Automation, Pod vodárenskou věží 4, 18208, Prague, Czech Republic

    Evgenia Suzdaleva & Ivan Nagy

  2. Faculty of Transportation Sciences, Czech Technical University, Na Florenci 25, 11000, Prague, Czech Republic

    Ivan Nagy

Authors
  1. Evgenia Suzdaleva
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  2. Ivan Nagy
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Corresponding author

Correspondence to Evgenia Suzdaleva .

Editor information

Editors and Affiliations

  1. University of Chemical Technology and Metalurgy, Sofia, Bulgaria

    M Hadjiski

  2. Department of Bioinformatics and Mathematical Modelling, Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Sciences, Sofia, Bulgaria

    K T Atanassov

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Suzdaleva, E., Nagy, I. (2019). Mixture Initialization Based on Prior Data Visual Analysis. In: Hadjiski, M., Atanassov, K. (eds) Intuitionistic Fuzziness and Other Intelligent Theories and Their Applications. Studies in Computational Intelligence, vol 757. Springer, Cham. https://doi.org/10.1007/978-3-319-78931-6_3

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