Nonparametric Linear Feature Learning in Regression Through Regularisation

Understand

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.