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Comparison of Various Definitions of Proximity in Mixture Estimation

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    0461565 - ÚTIA 2017 RIV PT eng C - Conference Paper (international conference)
    Nagy, Ivan - Suzdaleva, Evgenia - Pecherková, Pavla
    Comparison of Various Definitions of Proximity in Mixture Estimation.
    Proceedings of the 13th International Conference on Informatics in Control, Automation and Robotics (ICINCO 2016). Setubal: SCITEPRESS, 2016, s. 527-534. ISBN 978-989-758-198-4.
    [International Conference on Informatics in Control, Automation and Robotics /13./ (ICINCO 2016). Lisbon (PT), 29.07.2016-31.07.2016]
    R&D Projects: GA ČR(CZ) GA15-03564S
    Institutional support: RVO:67985556
    Keywords : classification * recursive mixture estimation * proximity * Bayesian methods * mixture based clustering
    Subject RIV: BB - Applied Statistics, Operational Research
    http://library.utia.cas.cz/separaty/2016/ZS/suzdaleva-0461565.pdf

    Classification is one of the frequently demanded tasks in data analysis. There exists a series of approaches in this area. This paper is oriented towards classification using the mixture model estimation, which is based on detection of density clusters in the data space and fitting the component models to them. A chosen function of proximity of the actually measured data to individual mixture components and the component shape play a significant role in solving the mixture-based classification task. This paper considers definitions of the proximity for several types of distributions describing the mixture components and compares their properties with respect to speed and quality of the resulting estimation interpreted as a classification task. Normal, exponential and uniform distributions as the most important models used for describing both Gaussian and non-Gaussian data are considered. Illustrative experiments with results of the comparison are provided.
    Permanent Link: http://hdl.handle.net/11104/0261344

     
     
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