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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
Algorithm design and analysis, Hidden Markov models, Parameter estimation, Fading, Iterative algorithms, Convergence, Maximum likelihood estimation, Numerical simulation, Expectation-maximization algorithms, Clustering algorithms,model selection., Maximum weighted likelihood, weight design, extended expectation-maximization algorithm
"On Weight Design of Maximum Weighted Likelihood and an Extended EM Algorithm", IEEE Transactions on Knowledge & Data Engineering, vol. 18, no. , pp. 1429-1434, October 2006, doi:10.1109/TKDE.2006.163
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