Comput Math Methods Med - A batch rival penalized expectation-maximization algorithm for Gaussian mixture clustering with automatic model selection.

Tópicos

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Resumo

Within the learning framework of maximum weighted likelihood (MWL) proposed by Cheung, 2004 and 2005, this paper will develop a batch Rival Penalized Expectation-Maximization (RPEM) algorithm for density mixture clustering provided that all observations are available before the learning process. Compared to the adaptive RPEM algorithm in Cheung, 2004 and 2005, this batch RPEM need not assign the learning rate analogous to the Expectation-Maximization (EM) algorithm (Dempster et al., 1977), but still preserves the capability of automatic model selection. Further, the convergence speed of this batch RPEM is faster than the EM and the adaptive RPEM in general. The experiments show the superior performance of the proposed algorithm on the synthetic data and color image segmentation.

Resumo Limpo

within learn framework maximum weight likelihood mwl propos cheung paper will develop batch rival penal expectationmaxim rpem algorithm densiti mixtur cluster provid observ avail learn process compar adapt rpem algorithm cheung batch rpem need assign learn rate analog expectationmaxim em algorithm dempster et al still preserv capabl automat model select converg speed batch rpem faster em adapt rpem general experi show superior perform propos algorithm synthet data color imag segment

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