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The recent Maximum Weighted Likelihood (MWL) [18], [19] has provided a general learning paradigm for density-mixture model selection and learning, in which weight design, however, is a key issue. This paper will therefore explore such a design, and through which a heuristic extended Expectation-Maximization (X-EM) algorithm is presented accordingly. Unlike the EM algorithm [1], the X-EM algorithm is able to perform model selection by fading the redundant components out from a density mixture, meanwhile estimating the model parameters appropriately. The numerical simulations demonstrate the efficacy of our algorithm.
Maximum weighted likelihood, weight design, extended expectation-maximization algorithm, model selection.

Z. Zhang and Y. Cheung, "On Weight Design of Maximum Weighted Likelihood and an Extended EM Algorithm," in IEEE Transactions on Knowledge & Data Engineering, vol. 18, no. , pp. 1429-1434, 2006.
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