Optimization transfer using surrogate objective functions
Optimization transfer using surrogate objective functions
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DOI：
10.2307/1390605
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发表时间：
2000-03-01
影响因子：
2.4
通讯作者：
Yang, I
中科院分区：
文献类型：
--
作者：
Lange, K;Hunter, DR;Yang, I
众所周知的EM算法是依赖于不完整或缺失数据的概念的优化传输算法。通过调用凸性参数，可以构建各种其他不涉及丢失数据的优化传输算法。这些算法都依赖于一个优化或优化功能，作为目标函数的替代品。优化代理函数可以使目标函数朝着正确的方向前进。本文通过统计文献中的一些具体例子来说明这一一般原则。由于优化转移算法往往表现出EM算法的收敛速度慢，两种方法加速优化转移的讨论和评估的背景下，具体的问题。
The well-known EM algorithm is an optimization transfer algorithm that depends on the notion of incomplete or missing data. By invoking convexity arguments, one can construct a variety of other optimization transfer algorithms that do not involve missing data. These algorithms all rely on a majorizing or minorizing function that serves as a surrogate for the objective function. Optimizing the surrogate function drives the objective function in the correct direction. This article illustrates this general principle by a number of specific examples drawn from the statistical literature. Because optimization transfer algorithms often exhibit the slow convergence of EM algorithms, two methods of accelerating optimization transfer are discussed and evaluated in the context of specific problems.