Poster
Time-dependent spatially varying graphical models, with application to brain fMRI data analysis
Kristjan Greenewald · Seyoung Park · Shuheng Zhou · Alexander Giessing
Pacific Ballroom #35
Keywords: [ Model Selection and Structure Learning ] [ Graphical Models ] [ Large Deviations and Asymptotic Analysis ] [ Sparsity and Compressed Sensing ] [ Frequentist Statistics ] [ Regularization ] [ Sparse Coding and Dimensionality Expansion ]
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.
Live content is unavailable. Log in and register to view live content