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Issue No.10 - October (2006 vol.18)
pp: 1429-1434
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.
INDEX TERMS
Maximum weighted likelihood, weight design, extended expectation-maximization algorithm, model selection.
CITATION
Zhenyue Zhang, Yiu-ming Cheung, "On Weight Design of Maximum Weighted Likelihood and an Extended EM Algorithm", IEEE Transactions on Knowledge & Data Engineering, vol.18, no. 10, pp. 1429-1434, October 2006, doi:10.1109/TKDE.2006.163
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