The posterior selection method for hyperparameters in regularized least squares method
Yanxin Zhang1
Jing Chen2
Yawen Mao1
Quanmin Zhu3
1.School of Science,Jiangnan University,Wuxi 214122,Jiangsu,China2.School of Science,Jiangnan University,Wuxi 214122,Jiangsu,China;The Science and Technology on Near-Surface Detection Laboratory,Wuxi 214122,Jiangsu,China3.Department of Engineering Design and Mathematics,University of the West of England,Bristol BS161QY,UK
摘要：The selection of hyperparameters in regularized least squares plays an important role in large-scale system identification.The traditional methods for selecting hyperparameters are based on experience or marginal likelihood maximization method,which are inaccurate or computationally expensive.In this paper,two posterior methods are proposed to select hyperparameters based on different prior knowledge(constraints),which can obtain the optimal hyperparameters using the optimization theory.Moreover,we also give the theoretical optimal constraints,and verify its effectiveness.Numerical simulation shows that the hyperparameters and parameter vector estimate obtained by the proposed methods are the optimal ones.
机标关键词：methodhyperparametersleastposteriorregularizedselectionsquares
论文发表日期：2024-05-05
在线出版日期：2025-08-15（本平台首次上网日期，不代表文献的发表时间）
页数：11( 184-194 )
英文信息