Feature-space selection with banded ridge regression.

ABSTRACT: 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, eff

SUBMITTER: Dupre la Tour T 

PROVIDER: S-EPMC9807218 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature