On Weight Design of Maximum Weighted Likelihood and an Extended EM Algorithm

Authors:
Zhenyue Zhang;Yiu-ming Cheung
Affiliations:
-;IEEE
Venue:
IEEE Transactions on Knowledge and Data Engineering
Year:
2006

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Abstract

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.