Sparse plus low-rank autoregressive identification in neuroimaging time series
- Resource Type
- Conference
- Authors
- Liegeois, Raphael; Mishra, Bamdev; Zorzi, Mattia; Sepulchre, Rodolphe
- Source
- 2015 54th IEEE Conference on Decision and Control (CDC) Decision and Control (CDC), 2015 IEEE 54th Annual Conference on. :3965-3970 Dec, 2015
- Subject
- Robotics and Control Systems
Yttrium
Graphical models
Covariance matrices
Symmetric matrices
Optimized production technology
Neuroimaging
Mathematical model
- Language
This paper considers the problem of identifying multivariate autoregressive (AR) sparse plus low-rank graphical models. Based on a recent problem formulation, we use the alternating direction method of multipliers (ADMM) to solve it efficiently as a convex program for sizes encountered in neuroimaging applications. We apply this algorithm on synthetic and real neuroimaging datasets with a specific focus on the information encoded in the low-rank structure of our model. In particular, we illustrate that this information captures the spatio-temporal structure of the original data, generalizing classical component analysis approaches.