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Joint Learning of Multiple Sparse Matrix Gaussian Graphical Models

Feihu Huang
Feihu Huang
Songcan Chen (陈松灿)
Songcan Chen (陈松灿)

IEEE transactions on neural networks and learning systems, Volume PP, Issue 99, 2015.

Cited by: 12|Bibtex|Views4|DOI:https://doi.org/10.1109/TNNLS.2014.2384201
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Other Links: pubmed.ncbi.nlm.nih.gov|academic.microsoft.com|dblp.uni-trier.de
Keywords:
sparsistency.precision matrixconsistencygaussian graphical modelsmatrix normal distributionMore(4+)

Abstract:

We consider joint learning of multiple sparse matrix Gaussian graphical models and propose the joint matrix graphical Lasso to discover the conditional independence structures among rows (columns) in the matrix variable under distinct conditions. The proposed approach borrows strength across the different graphical models and is based on ...More

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