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Sparse plus low-rank autoregressive identification in neuroimaging time series
[article]
2015
arXiv
pre-print
This paper considers the problem of identifying multivariate autoregressive (AR) sparse plus low-rank graphical models. Based on the corresponding problem formulation recently presented, we use the alternating direction method of multipliers (ADMM) to efficiently solve it and scale it to sizes encountered in neuroimaging applications. We apply this decomposition on synthetic and real neuroimaging datasets with a specific focus on the information encoded in the low-rank structure of our model.
arXiv:1503.08639v1
fatcat:5jnbo6a2andvnlamxgbfnjtclq