Equivalence of Graphical Lasso and Thresholding for Sparse Graphs
Authors: Somayeh Sojoudi
JMLR 2016 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Simulations on random systems are provided in Section 3. Two case studies on fMRI data and electrical circuits are conducted in Sections 4 and 5, respectively. |
| Researcher Affiliation | Academia | Somayeh Sojoudi, EMAIL Department of Electrical Engineering and Computer Sciences University of California, Berkeley |
| Pseudocode | No | The paper describes mathematical derivations and conditions for equivalence. It does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not explicitly provide concrete access to source code for the methodology described in this paper. |
| Open Datasets | Yes | These fMRI data sets are borrowed form Vertes et al. (2012). |
| Dataset Splits | No | The paper mentions data properties like 'Each data set includes 134 samples of the low frequency oscillations, taken at 140 cortical brain regions' and 'For r = 99 and 10 different trials, we have calculated the sample covariance matrices', but does not provide specific train/test/validation dataset splits. |
| Hardware Specification | No | The paper does not provide specific hardware details used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers needed to replicate the experiment. |
| Experiment Setup | Yes | We choose the regularization parameter λ in the graphical lasso algorithm and the level of thresholding in such a way that they both lead to graphs with n - 1 = 139 edges. |