Functional MRI analysis with sparse models

Abstract

Sparse models embed variable selection into model learning (e.g., by using l1-norm regularizer). In small-sample high-dimensional problems, this leads to improved generalization accuracy combined with interpretability, which is important in scientific applications such as biology. In this paper, we summarize our recent work on sparse models, including both sparse regression and sparse Gaussian Markov Random Fields (GMRF), in neuroimaging applications, such as functional MRI data analysis, where the central objective is to gain a better insight into brain functioning, besides just learning predictive models of mental states from imaging data. © 2013 Springer-Verlag.