Node-Based Learning of Multiple Gaussian Graphical Models
Authors: Karthik Mohan, Palma London, Maryam Fazel, Daniela Witten, Su-In Lee
JMLR 2014 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our proposal is illustrated on synthetic data, a webpage data set, and a brain cancer gene expression data set. Sections 6 and 7 are titled 'Simulation Study' and 'Real Data Analysis' respectively, providing empirical validation. |
| Researcher Affiliation | Academia | All authors are affiliated with the University of Washington, across different departments. Email domains are @uw.edu and @cs.washington.edu, which are academic. |
| Pseudocode | Yes | The paper contains 'Algorithm 1: ADMM algorithm for the PNJGL optimization problem (6)' and 'Algorithm 2: ADMM algorithm for the CNJGL optimization problem (7)'. |
| Open Source Code | Yes | Matlab code implementing CNJGL and PNJGL is available at http://faculty.washington. edu/mfazel/, http://www.biostat.washington.edu/~dwitten/software.html, and http://suinlee.cs.washington.edu/software. |
| Open Datasets | Yes | Our proposal is illustrated on synthetic data, a webpage data set, and a brain cancer gene expression data set... We applied PNJGL and CNJGL to the university webpages data set from the World Wide Knowledge Base project at Carnegie Mellon University. This data set was pre-processed by Cardoso-Cachopo (2009)... We applied the proposed methods to a publicly available gene expression data set... We downloaded the raw data in .CEL format from the The Caner Genome Atlas (TCGA) website. |
| Dataset Splits | Yes | We performed 5-fold cross-validation of the log-likelihood... for PNJGL, FGL, CNJGL, GGL, and GL, using a range of tuning parameters. |
| Hardware Specification | Yes | In a small example with p = 30, run on an Intel Xeon X3430 2.4Ghz CPU, the interior point method (using cvx, which calls Sedumi) takes 7 minutes to run, while the ADMM algorithm for PNJGL, coded in Matlab, takes only 0.58 seconds. |
| Software Dependencies | No | The paper mentions 'modeling environment cvx', 'conic interior-point solvers such as Se Du Mi or SDPT3', 'Matlab', 'SFNG functions in Matlab (George, 2007)', and 'Com Bat (Johnson and Li, 2006)'. However, no specific version numbers are provided for these software components. |
| Experiment Setup | Yes | We set µ = 5, ρ = 0.5 and tmax = 1000 in the PNJGL and CNJGL algorithms. In our implementation of these algorithms, the stopping criterion for the inner loop (corresponding to a fixed ρ) is ... ϵ is a tolerance that is chosen in our experiments to equal 10 4. ... Initialize: Primal variables to the identity matrix and dual variables to the zero matrix. ... PNJGL was performed with λ1 = 0 and λ2 = 2. ... CNJGL was performed with λ1 = 13 and λ2 = 410. |