Variable projection algorithms with sparse constraint for separable nonlinear models
Hui-Lang Xu1
Guang-Yong Chen1
Si-Qing Cheng2
Min Gan2
Jing Chen3
1.College of Computer and Data Science,Fuzhou University,Fuzhou 350116,Fujian,China;Fujian Key Laboratory of Network Computing and Intelligent Information Processing,Fuzhou University,Fuzhou 350116,Fujian,China;Key Laboratory of Intelligent Metro of Universities in Fujian,Fuzhou University,Fuzhou 350116,Fujian,China;Engineering Research Center of Big Data Intelligence,Ministry of Education,Fuzhou University,Fuzhou 350116,Fujian,China2.College of Computer and Data Science,Fuzhou University,Fuzhou 350116,Fujian,China3.School of Science,Jiangnan University,Wuxi 214122,Jiangsu,China
摘要：Separable nonlinear models are widely used in various fields such as time series analysis,system modeling,and machine learning,due to their flexible structures and ability to capture nonlinear behavior of data.However,identifying the parameters of these models is challenging,especially when sparse models with better interpretability are desired by practitioners.Previous theoretical and practical studies have shown that variable projection(VP)is an efficient method for identifying separable nonlinear models,but these are based on L2 penalty of model parameters,which cannot be directly extended to deal with sparse constraint.Based on the exploration of the structural characteristics of separable models,this paper proposes gradient-based and trust-region-based variable projection algorithms,which mainly solve two key problems:how to eliminate linear parameters under sparse constraint;and how to deal with the coupling relationship between linear and nonlinear parameters in the model.Finally,numerical experiments on synthetic data and real time series data are conducted to verify the effectiveness of the proposed algorithms.
机标关键词：projectionmodelswithalgorithmsconstraintnonlinearseparablesparse
论文发表日期：2024-02-05
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：12( 135-146 )
英文信息