It has been shown that L1-norm regularization does not recover sparse solutions in a Laplacian-constrained Gaussian Markov Random Field setting. sparseGraph provides a method to estimate sparse graphs via nonconvex regularization functions.
You can install the development version from GitHub:
> devtools::install_github("mirca/sparseGraph")
On MS Windows environments, make sure to install the most recent version
of Rtools.
We welcome all sorts of contributions. Please feel free to open an issue to report a bug or discuss a feature request.
If you made use of this software please consider citing:
-
J. Ying, J. V. de M. Cardoso, D. P. Palomar (2020). Nonconvex Sparse Graph Learning under Laplacian-structured Graphical Model. Advances in Neural Information Processing Systems (NeurIPS’20).
-
J. Ying, J. V. de M. Cardoso, D. P. Palomar (2020). Does the l1-norm learn a sparse graphical model under Laplacian constraints? https://arxiv.org/abs/2006.14925.