Some equivalence relationships of regularized regressions
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Date
2018
Authors
Zhang, Y.
Thakar, J.
Topham, D.
Falsey, A.
Zeng D.
Qiu, X.
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Mark
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Vysoké učení technické v Brně, Fakulta strojního inženýrství, Ústav matematiky
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Abstract
Regularization is a powerful framework for solving ill-posed problem and preventing model overfitting in modern regression analysis. It is especially useful for high-dimensional or functional (infinite dimensional) regression models. In this paper, we construct two useful equivalence relationships for regularized regression: 1. An equivalence between regularized functional regression and regularized multi- variate regression. This equivalence provides a computationally efficient way to fit the concurrent functional regression model. 2. An equivalence of penalized multi- variate regression under a group of scaling transformation. This equivalence can be used to solve weighted principal component regression efficiently.
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Citation
Mathematics for Applications. 2018 vol. 7, č. 1, s. 3-10. ISSN 1805-3629
http://ma.fme.vutbr.cz/archiv/7_1/ma_7_1_1_zhang_et_al_final.pdf
http://ma.fme.vutbr.cz/archiv/7_1/ma_7_1_1_zhang_et_al_final.pdf
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Peer-reviewed
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en
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© Vysoké učení technické v Brně, Fakulta strojního inženýrství, Ústav matematiky