document: New EM derived from Kullback-Leibler divergence We introduce a new EM framework in which it is possible not only to optimize the model parameters but also the number of model components . A key feature of our approach is that we use nonparametric density estimation to improve parametric density estimation in the EM framework . While the classical EM algorithm estimates model parameters empirically using the data points themselves , we estimate them using nonparametric density estimates . There exist many possible applications that require optimal adjustment of model components . We present experimental results in two domains . One is polygonal approximation of laser range data , which is an active research topic in robot navigation . The other is grouping of edge pixels to contour boundaries , which still belongs to unsolved problems in computer vision .
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