Reference : A kurtosis-based dynamic approach to Gaussian mixture modeling
Scientific journals : Article
Engineering, computing & technology : Computer science
A kurtosis-based dynamic approach to Gaussian mixture modeling
Vlassis, Nikos mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > >]
Likas, A. [> >]
IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans
Yes (verified by ORBilu)
[en] expectation-maximization (EM) algorithm ; Gaussian mixture modeling ; number of mixing kernels ; probability density function estimation ; total kurtosis ; weighted kurtosis
[en] We address the problem of probability density function estimation using a Gaussian mixture model updated with the expectation-maximization (EM) algorithm. To deal with the case of an unknown number of mixing kernels, we define a new measure for Gaussian mixtures, called total kurtosis, which is based on the weighted sample kurtoses of the kernels. This measure provides an indication of how well the Gaussian mixture fits the data. Then we propose a new dynamic algorithm for Gaussian mixture density estimation which monitors the total kurtosis at each step of the Ehl algorithm in order to decide dynamically on the correct number of kernels and possibly escape from local maxima. We show the potential of our technique in approximating unknown densities through a series of examples with several density estimation problems.

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