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Feature-space selection with banded ridge regression

Encoding models provide a powerful framework to identify the information represented in brain recordings. In this framework, a stimulus representation is expressed within a feature space and is used in a regularized linear regression to predict brain activity. To account for a potential complementar...

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Detalles Bibliográficos
Autores principales: la Tour, Tom Dupré, Eickenberg, Michael, Nunez-Elizalde, Anwar O., Gallant, Jack L.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9807218/
https://www.ncbi.nlm.nih.gov/pubmed/36334814
http://dx.doi.org/10.1016/j.neuroimage.2022.119728
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author la Tour, Tom Dupré
Eickenberg, Michael
Nunez-Elizalde, Anwar O.
Gallant, Jack L.
author_facet la Tour, Tom Dupré
Eickenberg, Michael
Nunez-Elizalde, Anwar O.
Gallant, Jack L.
author_sort la Tour, Tom Dupré
collection PubMed
description Encoding models provide a powerful framework to identify the information represented in brain recordings. In this framework, a stimulus representation is expressed within a feature space and is used in a regularized linear regression to predict brain activity. To account for a potential complementarity of different feature spaces, a joint model is fit on multiple feature spaces simultaneously. To adapt regularization strength to each feature space, ridge regression is extended to banded ridge regression, which optimizes a different regularization hyperparameter per feature space. The present paper proposes a method to decompose over feature spaces the variance explained by a banded ridge regression model. It also describes how banded ridge regression performs a feature-space selection, effectively ignoring non-predictive and redundant feature spaces. This feature-space selection leads to better prediction accuracy and to better interpretability. Banded ridge regression is then mathematically linked to a number of other regression methods with similar feature-space selection mechanisms. Finally, several methods are proposed to address the computational challenge of fitting banded ridge regressions on large numbers of voxels and feature spaces. All implementations are released in an open-source Python package called Himalaya.
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spelling pubmed-98072182023-01-02 Feature-space selection with banded ridge regression la Tour, Tom Dupré Eickenberg, Michael Nunez-Elizalde, Anwar O. Gallant, Jack L. Neuroimage Article Encoding models provide a powerful framework to identify the information represented in brain recordings. In this framework, a stimulus representation is expressed within a feature space and is used in a regularized linear regression to predict brain activity. To account for a potential complementarity of different feature spaces, a joint model is fit on multiple feature spaces simultaneously. To adapt regularization strength to each feature space, ridge regression is extended to banded ridge regression, which optimizes a different regularization hyperparameter per feature space. The present paper proposes a method to decompose over feature spaces the variance explained by a banded ridge regression model. It also describes how banded ridge regression performs a feature-space selection, effectively ignoring non-predictive and redundant feature spaces. This feature-space selection leads to better prediction accuracy and to better interpretability. Banded ridge regression is then mathematically linked to a number of other regression methods with similar feature-space selection mechanisms. Finally, several methods are proposed to address the computational challenge of fitting banded ridge regressions on large numbers of voxels and feature spaces. All implementations are released in an open-source Python package called Himalaya. 2022-12-01 2022-11-08 /pmc/articles/PMC9807218/ /pubmed/36334814 http://dx.doi.org/10.1016/j.neuroimage.2022.119728 Text en https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) )
spellingShingle Article
la Tour, Tom Dupré
Eickenberg, Michael
Nunez-Elizalde, Anwar O.
Gallant, Jack L.
Feature-space selection with banded ridge regression
title Feature-space selection with banded ridge regression
title_full Feature-space selection with banded ridge regression
title_fullStr Feature-space selection with banded ridge regression
title_full_unstemmed Feature-space selection with banded ridge regression
title_short Feature-space selection with banded ridge regression
title_sort feature-space selection with banded ridge regression
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9807218/
https://www.ncbi.nlm.nih.gov/pubmed/36334814
http://dx.doi.org/10.1016/j.neuroimage.2022.119728
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