Pii: S0031-3203(01)00133-9
نویسنده
چکیده
We present a novel method for representing “extruded” distributions. An extruded distribution is an M -dimensional manifold in the parameter space of the component distribution. Representations of that manifold are “continuous mixture models”. We present a method for forming one-dimensional continuous Gaussian mixture models of sampled extruded Gaussian distributions via ridges of goodness-of-#t. Using Monte Carlo simulations and ROC analysis, we explore the utility of a variety of binning techniques and goodness-of-#t functions. We demonstrate that extruded Gaussian distributions are more accurately and consistently represented by continuous Gaussian mixture models than by #nite Gaussian mixture models formed via maximum likelihood expectation maximization. ? 2002 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.
منابع مشابه
Gaussian mixture parameter estimation with known means and unknown class-dependent variances
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