Open AccessOn an unsupervised method for parameter selection for the elastic net
Author(s) -
Željko Kereta,
Valeriya Naumova
Publication year - 2022
Publication title -
mathematics in engineering
Language(s) - English
Resource type - Journals
ISSN - 2640-3501
DOI - 10.3934/mine.2022053
Subject(s) - tikhonov regularization , elastic net regularization , regularization (linguistics) , regularization perspectives on support vector machines , computation , backus–gilbert method , model selection , computer science , statistical learning theory , noisy data , algorithm , artificial intelligence , mathematics , mathematical optimization , machine learning , inverse problem , feature selection , mathematical analysis , support vector machine
Despite recent advances in regularization theory, the issue of parameter selection still remains a challenge for most applications. In a recent work the framework of statistical learning was used to approximate the optimal Tikhonov regularization parameter from noisy data. In this work, we improve their results and extend the analysis to the elastic net regularization. Furthermore, we design a data-driven, automated algorithm for the computation of an approximate regularization parameter. Our analysis combines statistical learning theory with insights from regularization theory. We compare our approach with state-of-the-art parameter selection criteria and show that it has superior accuracy.