The recent maximum weighted likelihood (MWL) 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, 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
['Zhenyue Zhang', 'Yiu-ming Cheung']
On Weight Design of Maximum Weighted Likelihood and an Extended EM Algorithm