Sparse Learning for Variable Selection with Structures and Nonlinearities

TL;DR

This thesis explores machine learning techniques for automated variable selection to create sparse, interpretable, and cost-effective predictive models that combat overfitting and reduce computational and data collection costs.

Contribution

It introduces methods for sparse learning that incorporate structures and nonlinearities, enhancing variable selection in predictive modeling.

Findings

Sparse models improve interpretability and reduce overfitting.

Selected variables lead to lower computational costs.

Methods effectively handle structured and nonlinear data.

Abstract

In this thesis we discuss machine learning methods performing automated variable selection for learning sparse predictive models. There are multiple reasons for promoting sparsity in the predictive models. By relying on a limited set of input variables the models naturally counteract the overfitting problem ubiquitous in learning from finite sets of training points. Sparse models are cheaper to use for predictions, they usually require lower computational resources and by relying on smaller sets of inputs can possibly reduce costs for data collection and storage. Sparse models can also contribute to better understanding of the investigated phenomenons as they are easier to interpret than full models.