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An exact line search scheme to accelerate the EM algorithm: Application to Gaussian mixture models identification
Journal of Computational Science ( IF 3.3 ) Pub Date : 2020-01-13 , DOI: 10.1016/j.jocs.2019.101073
Wentao Xiang , Ahmad Karfoul , Chunfeng Yang , Huazhong Shu , Régine Le Bouquin Jeannès

This paper tackles the slowness issue of the well-known expectation-maximization (EM) algorithm in the context of Gaussian mixture models. To cope with this slowness problem, an Exact Line Search scheme is proposed. It is based on exact computation of the step size required to jump, for a given search direction, towards the final solution. Computing this exact step size is easily done by only rooting a second-order polynomial computed from the initial log-likelihood maximization problem. Numerical results using both simulated and real dataset showed the efficiency of the proposed exact line search scheme when applied to the conventional EM algorithm as well as the anti-annealing based acceleration techniques based on either the EM or the expectation conjugate gradient algorithm.



中文翻译:

加速EM算法的精确线搜索方案:在高斯混合模型识别中的应用

本文在高斯混合模型的背景下解决了众所周知的期望最大化(EM)算法的慢度问题。为了解决该慢度问题,提出了精确线搜索方案。它基于对给定搜索方向跳到最终解所需的步长的精确计算。仅通过对从初始对数似然最大化问题计算出的二阶多项式求根即可轻松计算出此精确步长。使用模拟数据集和实际数据集的数值结果表明,将其应用到常规EM算法以及基于EM或期望共轭梯度算法的基于反退火的加速技术时,所提出的精确线搜索方案的效率较高。

更新日期:2020-01-13
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