Functional MRI Analysis with Sparse Models
Abstract
Sparse models embed variable selection into model learning (e.g., by using l _1-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.
Cite
Text
Rish. "Functional MRI Analysis with Sparse Models." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2013. doi:10.1007/978-3-642-40994-3_43Markdown
[Rish. "Functional MRI Analysis with Sparse Models." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2013.](https://mlanthology.org/ecmlpkdd/2013/rish2013ecmlpkdd-functional/) doi:10.1007/978-3-642-40994-3_43BibTeX
@inproceedings{rish2013ecmlpkdd-functional,
title = {{Functional MRI Analysis with Sparse Models}},
author = {Rish, Irina},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2013},
pages = {632-636},
doi = {10.1007/978-3-642-40994-3_43},
url = {https://mlanthology.org/ecmlpkdd/2013/rish2013ecmlpkdd-functional/}
}