Joint Estimation of Multiple Conditional Gaussian Graphical Models

IEEE Transactions on Neural Networks and Learning Systems, pp. 3034-3046, 2017.

Cited by: 5|Bibtex|Views8|DOI:https://doi.org/10.1109/TNNLS.2017.2710090
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Other Links: pubmed.ncbi.nlm.nih.gov|dblp.uni-trier.de|academic.microsoft.com

Abstract:

In this paper, we propose a joint conditional graphical Lasso to learn multiple conditional Gaussian graphical models, also known as Gaussian conditional random fields, with some similar structures. Our model builds on the maximum likelihood method with the convex sparse group Lasso penalty. Moreover, our model is able to model multiple m...More

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