CUED Publications database

Sparse plus low-rank autoregressive identification in neuroimaging time series

Liegeois, R and Mishra, B and Zorzi, M and Sepulchre, R (2015) Sparse plus low-rank autoregressive identification in neuroimaging time series. In: UNSPECIFIED pp. 3965-3970..

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Abstract

© 2015 IEEE. 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.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Subjects: UNSPECIFIED
Divisions: Div F > Control
Depositing User: Cron Job
Date Deposited: 17 Jul 2017 19:12
Last Modified: 24 Aug 2017 01:30
DOI: