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A Block Coordinate Descent Algorithm for Sparse Gaussian Graphical Model Inference with Laplacian Constraints

: Liu, Tianyi; Hoang, Minh Trinh; Yang, Yang; Pesavento, Marius


Institute of Electrical and Electronics Engineers -IEEE-; IEEE Signal Processing Society:
IEEE 8th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2019. Proceedings : December 15-18, 2019, Guadeloupe
Piscataway, NJ: IEEE, 2019
ISBN: 978-1-7281-5549-4
ISBN: 978-1-7281-5548-7
ISBN: 978-1-7281-5550-0
International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP) <8, 2019, Guadeloupe>
Fraunhofer ITWM ()

We consider the problem of inferring sparse Gaussian graphical models with Laplacian constraints, which can also be viewed as learning a graph Laplacian such that the observed graph signals are smooth with respect to it. A block coordinate descent algorithm is proposed for the resulting linearly constrained log-determinant maximum likelihood estimation problem with sparse regularization. Simulation results on synthetic data show the efficiency of our proposed algorithm.