Hierarchical sparse brain network estimation
Brain networks explore the dependence relationships between brain regions under consideration through the estimation of the precision matrix. An approach based on linear regression is adopted here for estimating the partial correlation matrix from functional brain imaging data. Knowing that brain networks are sparse and hierarchical, the l1-norm penalized regression has been used to estimate sparse brain networks. Although capable of including the sparsity information, the l1-norm penalty alone...[Show more]
|Collections||ANU Research Publications|
|Source:||IEEE International Workshop on Machine Learning for Signal Processing, MLSP|
|01_Seghouane_Hierarchical_sparse_brain_2012.pdf||321.19 kB||Adobe PDF||Request a copy|
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