Graphical Model Structure Learning with L1-Regularization

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Authors Mark Schmidt
Journal/Conference Name PhD Thesis
Paper Category
Paper Abstract This work looks at fitting probabilistic graphical models to data when the structure is not known. The main tool to do this is L1-regularization and the more general group L1-regularization. We describe limited-memory quasi-Newton methods to solve optimization problems with these types of regularizers, and we examine learning directed acyclic graphical models with L1-regularization, learning undirected graphical models with group L1-regularization, and learning hierarchical loglinear models with overlapping group L1-regularization.
Date of publication 2010
Code Programming Language MATLAB
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