DNNLasso: Scalable Graph Learning for Matrix-Variate Data

arXiv:2403.02608v1 Announce Type: new
Abstract: We consider the problem of jointly learning row-wise and column-wise dependencies of matrix-variate observations, which are modelled separately by two precision matrices. Due to the complicated structure of Kronecker-product precision matrices in the commonly used matrix-variate Gaussian graphical models, a sparser Kronecker-sum structure was proposed recently based on the Cartesian product of graphs. However, existing methods for estimating Kronecker-sum structured precision matrices do not scale well to large scale datasets. In this paper, we introduce …

arxiv cs.lg data graph graph learning math.oc matrix scalable type