Nonparametric Linear Feature Learning in Regression Through Regularisation

arXiv:2307.12754v3 Announce Type: replace-cross
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 linear feature learning with …

abstract arxiv automated context cs.ai cs.lg data feature feature selection focus index information linear math.st multi-index non-parametric parametric regression representation representation learning role stat.me stat.th struggle study supervised learning through type