Graphical Model Structure Learning with L1-Regularization

View Researcher II's Other Codes

Disclaimer: The provided code links for this paper are external links. Science Nest has no responsibility for the accuracy, legality or content of these links. Also, by downloading this code(s), you agree to comply with the terms of use as set out by the author(s) of the code(s).

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

Copyright Researcher II 2022