Estimation of Sparse Gaussian Graphical Models with Hidden Clustering Structure
Authors: Meixia Lin, Defeng Sun, Kim-Chuan Toh, Chengjing Wang
JMLR 2024 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical experiments on both synthetic data and real data demonstrate the good performance of our model, as well as the efficiency and robustness of our proposed algorithm. |
| Researcher Affiliation | Academia | Meixia Lin EMAIL Engineering Systems and Design Singapore University of Technology and Design... Defeng Sun EMAIL Department of Applied Mathematics The Hong Kong Polytechnic University... Kim-Chuan Toh EMAIL Department of Mathematics and Institute of Operations Research and Analytics National University of Singapore... Chengjing Wang EMAIL School of Mathematics Southwest Jiaotong University... |
| Pseudocode | Yes | Algorithm 1 : s GS-ADMM... Algorithm 2 : p ALM... Algorithm 3 : SSN |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. It only references a third-party tool's link (https://CRAN.R-project.org/package=spectralGraphTopology) for visualization. |
| Open Datasets | Yes | The RNA-Seq Cancer Genome Atlas Research Network (Weinstein et al., 2013; Kumar et al., 2020)... We use the Animals data set (Kemp and Tenenbaum, 2008; Egilmez et al., 2017; Kumar et al., 2020)... We consider the Zoo data set from the UCI Machine Learning Repository... |
| Dataset Splits | No | The paper describes generating `p` independent samples for synthetic data and using full real datasets for estimation, but does not specify training/test/validation splits as the methodology involves graphical model estimation on the entire dataset rather than typical supervised learning splits. |
| Hardware Specification | Yes | All experiments are implemented in Matlab R2022b on a windows workstation (16-core, Intel Xeon Gold 6244 @ 3.60GHz, 128 G RAM). |
| Software Dependencies | Yes | All experiments are implemented in Matlab R2022b on a windows workstation (16-core, Intel Xeon Gold 6244 @ 3.60GHz, 128 G RAM). |
| Experiment Setup | Yes | In each method, we always have one tuning parameter ρ, which will be selected by grid search. Specifically, for our estimator, we select ρ in the range of 5 10 5 to 5 10 3 with 20 equally divided grid points... we fix the iteration number of the s GS-ADMM in Phase I to be 200... we stop the algorithm when max{RP , RD, RC} < Tol, with Tol = 10 6 as the default. |