Joint learning of multiple Granger causal networks via non-convex regularizations: inference of group-level brain connectivity

DOI10.1016/j.neunet.2022.02.005zbMath1530.92011arXiv2105.07196WikidataQ114950230 ScholiaQ114950230MaRDI QIDQ6072512

Parinthorn Manomaisaowapak, Jitkomut Songsiri

Publication date: 13 October 2023

Published in: Neural Networks (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2105.07196

zbMATH Keywords

Granger causalitynon-convex penaltycomposite penaltyeffective brain connectivity

Mathematics Subject Classification ID

Neural networks for/in biological studies, artificial life and related topics (92B20) Memory and learning in psychology (91E40)

Cites Work

• Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers
• Sparse inverse covariance estimation with the graphical lasso
• Joint estimation of precision matrices in heterogeneous populations
• Learning a common substructure of multiple graphical Gaussian models
• A note on adaptive group Lasso
• Regularized joint estimation of related vector autoregressive models
• Joint estimation of multiple graphical models
• Extended Bayesian information criteria for model selection with large model spaces
• The Joint Graphical Lasso for Inverse Covariance Estimation Across Multiple Classes
• Group sparse optimization via $\ell_{p,q}$ regularization
• Model Selection and Estimation in Regression with Grouped Variables
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