Note on the Equivalence of Orthogonalizing EM and Proximal Gradient Descent.

IF 2.3 3区工程技术 Q1 STATISTICS & PROBABILITY

Technometrics Pub Date : 2025-01-01 Epub Date: 2024-12-23 DOI:10.1080/00401706.2024.2430204

James Yang, Trevor Hastie

引用次数: 0

Abstract

Xiong et al. (2016) develop a method called orthogonalizing EM (OEM) to solve penalized regression problems for tall data. While OEM is developed in the context of the EM algorithm, we show that it is, in fact, an instance of proximal gradient descent, a popular first-order convex optimization algorithm.

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关于正交化EM与近端梯度下降的等价性的注记。

Xiong等人（2016）开发了一种称为正交化EM （OEM）的方法来解决高数据的惩罚回归问题。虽然OEM是在EM算法的背景下开发的，但我们表明它实际上是近端梯度下降的一个实例，这是一种流行的一阶凸优化算法。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Technometrics 管理科学-统计学与概率论

CiteScore

4.50

自引率

16.00%

发文量

审稿时长

>12 weeks

期刊介绍： Technometrics is a Journal of Statistics for the Physical, Chemical, and Engineering Sciences, and is published Quarterly by the American Society for Quality and the American Statistical Association.Since its inception in 1959, the mission of Technometrics has been to contribute to the development and use of statistical methods in the physical, chemical, and engineering sciences.