A kurtosis-based dynamic approach to Gaussian mixture modeling

@article{Vlassis1999AKD,
  title={A kurtosis-based dynamic approach to Gaussian mixture modeling},
  author={Nikos A. Vlassis and Aristidis Likas},
  journal={IEEE Trans. Systems, Man, and Cybernetics, Part A},
  year={1999},
  volume={29},
  pages={393-399}
}
We address the problem of probability density function estimation using a Gaussian mixture model updated with the expectationmaximization (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… CONTINUE READING
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