A Hybrid Algorithm with GA and DAEM
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.
Expectation maximization deterministic annealing genetic algorithm Gaussian mixture model maximum-likelihood estimation
Hong-Jie Wan Hao-Jiang Deng
College of Information Science and Technology Beijing University of Chemical Technology Beijing, Chi National Network New Media Engineering Research Center Institute of Acoustics, Chinese Academy of Sc
国际会议
重庆
英文
404-407
2011-01-21（万方平台首次上网日期，不代表论文的发表时间）