Vlassis and Likas : a Kurtosis - Based Dynamic Approach to Gaussian Mixture Modeling 3
نویسندگان
چکیده
| 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 deene a new measure for Gaussian mixtures, called total kurtosis, which is based on the weighted sample kur-toses of the kernels. This measure provides an indication of how well the Gaussian mixture ts the data. Then we propose a new dynamic algorithm for Gaussian mixture density estimation which monitors the total kurtosis at each step of the EM 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.
منابع مشابه
Vlassis and Likas : a Kurtosis - Based Dynamic Approach to Gaussian Mixture Modeling
| We address the problem of probability density function estimation using a Gaussian mixture model updated with the EM algorithm. To deal with the case of an unknown number of mixing kernels, we deene 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 ts t...
متن کاملA kurtosis-based dynamic approach to Gaussian mixture modeling
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 ...
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