Nonparametric Linear Feature Learning in Regression Through Regularisation
Abstract
Representation learning plays a crucial role in automated feature selection, particularly in the context of high-dimensional data, where non-parametric methods often struggle. In this study, we focus on supervised learning scenarios where the pertinent information resides within a lower-dimensional linear subspace of the data, namely the multi-index model. If this subspace were known, it would greatly enhance prediction, computation, and interpretation. To address this challenge, we propose a novel method for joint linear feature learning and non-parametric function estimation, aimed at more effectively leveraging hidden features for learning. Our approach employs empirical risk minimisation, augmented with a penalty on function derivatives, ensuring versatility. Leveraging the orthogonality and rotation invariance properties of Hermite polynomials, we introduce our estimator, named RegFeaL. By using alternative minimisation, we iteratively rotate the data to improve alignment with leading directions. We establish that the expected risk of our method converges in high-probability to the minimal risk under minimal assumptions and with explicit rates. Additionally, we provide empirical results demonstrating the performance of RegFeaL in various experiments.
Keywords
Multi-index model
Sparsity
Nonparametric regression
Regularised empirical risk minimisation
Alternating minimisation
Hermite orthogonal polynomials
MSC2020 subject classifications: Primary 62G08 62F10
secondary 65K10 multi-index model sparsity non-parametric regression regularised empirical risk minimisation alternating minimisation Hermite polynomials
MSC2020 subject classifications: Primary 62G08
62F10
secondary 65K10 multi-index model
sparsity
non-parametric regression
regularised empirical risk minimisation
alternating minimisation
Hermite polynomials
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