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应用于电磁场算法的三级加速及其基于外推的变型

Triple jump acceleration for the EM algorithm and its extrapolation-based variants
课程网址: http://videolectures.net/mlss06tw_huang_tjaai/  
主讲教师: Han-Shen Huang
开课单位: 中央研究院
开课时间: 2007-04-25
课程语种: 英语
中文简介:
艾特肯加速度是加速不动点迭代计算最常用的方法之一,包括电磁法。然而,它需要计算或近似电磁映射矩阵的雅可比矩阵,这对于复杂的模型来说是难以解决的。我们将介绍我们当前的研究主题,三级跳加速度,通过近似他们的雅可比方程来加速em及其一些基于外推的变体。三级跳框架的一个优点是,我们可以直接使用em及其基于外推法的变体作为黑盒,并轻松实现加速。我们可以使用相同的近似雅可比矩阵全局更新参数向量,也可以基于具有不同雅可比矩阵的每个可分解分量局部更新参数向量。实验结果表明,对于各种概率模型,三阶跳方法均能持续加速EM、参数化EM(PEM)和自适应EM(AEM)。
课程简介: The Aitken's acceleration is one of the most commonly used method to speed up the fixed-point iteration computation, including the EM algorithm. However, it requires to compute or approximate the Jacobian of the EM mapping matrix, which can be intractable for complex models. We will present our current research topic, the triple-jump acceleration, to accelerate the EM and some of its extrapolation-based variants by approximating their Jacobians. One advantage of the triple jump framework is that we can directly use the EM and its extrapolation-based variants as black boxes and achieve acceleration easily. We can update parameter vectors globally with the same approximated Jacobian, or locally based on each decomposable component with different Jacobians. Experimental results show that the triple jump methods consistently accelerate EM, parameterized EM (pEM) and adaptive EM (aEM) for a variety of probabilistic models.
关 键 词: 迭代计算; 电磁算法; 雅可比矩阵; 概率模型
课程来源: 视频讲座网
最后编审: 2020-06-26:cxin
阅读次数: 38