Kereta, Zeljko;
Naumova, Valeriya;
(2022)
On an unsupervised method for parameter selection for the elastic net.
Mathematics in Engineering
, 4
(6)
pp. 1-36.
10.3934/mine.2022053.
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Abstract
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.
| Type: | Article |
|---|---|
| Title: | On an unsupervised method for parameter selection for the elastic net |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.3934/mine.2022053 |
| Publisher version: | https://doi.org/10.3934/mine.2022053 |
| Language: | English |
| Additional information: | © 2022 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0) |
| Keywords: | parameter selection, elastic net regularization, data-driven regularization, iterative thresholding, sub-gaussian vectors, matrix concentration inequalities |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10173716 |
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