Learning Sparse High-Dimensional Matrix-Valued Graphical Models From Dependent Data

arXiv:2404.19073v1 Announce Type: cross
Abstract: We consider the problem of inferring the conditional independence graph (CIG) of a sparse, high-dimensional, stationary matrix-variate Gaussian time series. All past work on high-dimensional matrix graphical models assumes that independent and identically distributed (i.i.d.) observations of the matrix-variate are available. Here we allow dependent observations. We consider a sparse-group lasso-based frequency-domain formulation of the problem with a Kronecker-decomposable power spectral density (PSD), and solve it via an alternating direction method of multipliers (ADMM) approach. …

abstract arxiv cs.lg data distributed eess.sp graph independent matrix series stat.ml the matrix time series type work