Conjugate Gradient Acceleration of the EM Algorithm
利用共轭梯度加速 EM 算法AI 解读 ↓
← 返回论文列表与筛选
阅读说明
本页整理公开书目信息，不生成学术质量评分。AI 解读根据题名及所注明的摘要或原文片段生成，未完成全文核验，可能有误；具体假设、证明与结论请核对原文。摘要保留来源文本，未经模型改写。
DOI 首次登记不等于接收或发表日期。部分更正、评论等条目没有独立摘要；缺少可靠材料时保留待补状态。
引用量采用 OpenAlex 单条关联记录，不合计多个记录；关联记录可能包含正式发表版本，不等于此版本单独引用。缺失不视为零，引用指标不代表论文质量。
关注作者按书目中的完整姓名匹配，同名和姓名变体需人工辨别。相关论文先按共享细分方向和具体方法排列，再参考通用方法与发表日期。
分类依据与纠错
AI 根据所提供的论文材料归类：摘要聚焦通用优化算法，因子分析和重复测量示例不足以确定宏观因子或面板数据分类。
反馈分类问题
正在核对关联版本…
代码、数据与补充材料
正在核对对应版本的资源…
关注这篇论文的作者
利用共轭梯度加速 EM 算法
- 研究问题
- 如何缓解 EM 算法在最大似然计算中的收敛缓慢问题，并在参数数量较多时保持加速方法的实现简便性与计算可行性？
- 方法与思路
- 作者将 EM 更新近似解释为广义梯度，据此采用广义共轭梯度方法加速，并在缺失数据协方差估计、因子分析和重复测量中演示。
- 材料陈述的贡献
- 摘要称所考察例子的运算次数均减少，部分加速达十倍以上；另提出每次迭代成本较低的重复测量 EM 算法，但未评估其性能。
- 阅读关注点
- 应核对广义梯度解释的近似条件、加速步骤及停止准则，比较运算次数与实际耗时，并区分两种新算法各自获得的验证。
- 阅读边界
- OpenAlex 索引摘要未注明稿件版本；示例中的加速不能推广到所有 EM 问题，新重复测量算法的性能也未在摘要中得到验证。
原文摘要
The EM algorithm is a very popular and widely applicable algorithm for the computation of maximum likelihood estimates. Although its implementation is generally simple, the EM algorithm often exhibits slow convergence and is costly in some areas of application. Past attempts to accelerate the EM algorithm have most commonly been based on some form of Aitken acceleration. Here we propose an alternative method based on conjugate gradients. The key, as we show, is that the EM step can be viewed (approximately at least) as a generalized gradient, making it natural to apply generalized conjugate gradient methods in an attempt to accelerate the EM algorithm. The proposed method is relatively simple to implement and can handle Problems with a large number of parameters, an important feature of most EM algorithms. To demonstrate the effectiveness of the proposed acceleration method, we consider its application to several Problems in each of the following areas: estimation of a covariance matrix from incomplete multivariate normal data, confirmatory factor analysis, and repeated measures analysis. The examples in these areas demonstrate promise for the new acceleration method. In terms of Operation counts, for all of the examples considered the accelerated EM algorithm increases the speed of the EM algorithm, in some cases by a factor of 10 or more. In the context of repeated measures analysis, we give a new EM algorithm that, compared to earlier algorithms, can have a considerably smaller cost per iteration. We have not, however, attempted to evaluate the performance of this latter algorithm here.