14 March 2013 A hybrid algorithm with GA and DAEM
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Proceedings Volume 8768, International Conference on Graphic and Image Processing (ICGIP 2012); 87683D (2013) https://doi.org/10.1117/12.2011056
Event: 2012 International Conference on Graphic and Image Processing, 2012, Singapore, Singapore
Abstract
Although the expectation-maximization (EM) algorithm has been widely used for finding maximum likelihood estimation of parameters in probabilistic models, it has the problem of trapping by local maxima. To overcome this problem, the deterministic annealing EM (DAEM) algorithm was once proposed and had achieved better performance than EM algorithm, but it is not very effective at avoiding local maxima. In this paper, a solution is proposed by integrating GA and DAEM into one procedure to further improve the solution quality. The population based search of genetic algorithm will produce different solutions and thus can increase the search space of DAEM. Therefore, the proposed algorithm will reach better solution than just using DAEM. The algorithm retains the property of DAEM and gets the better solution by genetic operation. Experiment results on Gaussian mixture model parameter estimation demonstrate that the proposed algorithm can achieve better performance.
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HongJie Wan, HongJie Wan, HaoJiang Deng, HaoJiang Deng, XueWei Wang, XueWei Wang, } "A hybrid algorithm with GA and DAEM", Proc. SPIE 8768, International Conference on Graphic and Image Processing (ICGIP 2012), 87683D (14 March 2013); doi: 10.1117/12.2011056; https://doi.org/10.1117/12.2011056
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