Time-Dependent Spatially Varying Graphical Models, with Application to Brain fMRI Data Analysis
Abstract
In this work, we present an additive model for space-time data that splits the data into a temporally correlated component and a spatially correlated component. We model the spatially correlated portion using a time-varying Gaussian graphical model. Under assumptions on the smoothness of changes in covariance matrices, we derive strong single sample convergence results, confirming our ability to estimate meaningful graphical structures as they evolve over time. We apply our methodology to the discovery of time-varying spatial structures in human brain fMRI signals.
Cite
Text
Greenewald et al. "Time-Dependent Spatially Varying Graphical Models, with Application to Brain fMRI Data Analysis." Neural Information Processing Systems, 2017.Markdown
[Greenewald et al. "Time-Dependent Spatially Varying Graphical Models, with Application to Brain fMRI Data Analysis." Neural Information Processing Systems, 2017.](https://mlanthology.org/neurips/2017/greenewald2017neurips-timedependent/)BibTeX
@inproceedings{greenewald2017neurips-timedependent,
title = {{Time-Dependent Spatially Varying Graphical Models, with Application to Brain fMRI Data Analysis}},
author = {Greenewald, Kristjan and Park, Seyoung and Zhou, Shuheng and Giessing, Alexander},
booktitle = {Neural Information Processing Systems},
year = {2017},
pages = {5832-5840},
url = {https://mlanthology.org/neurips/2017/greenewald2017neurips-timedependent/}
}