Joint Learning of Multiple Sparse Matrix Gaussian Graphical Models

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
WOS EI
Other Links: pubmed.ncbi.nlm.nih.gov|academic.microsoft.com|dblp.uni-trier.de

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

Code:

Data:

Your rating :
0

 

Tags
Comments