Quadratic Sparse Gaussian Graphical Model Estimation Method for Massive Variables
Authors: Jiaqi Zhang, Meng Wang, Qinchi Li, Sen Wang, Xiaojun Chang, Beilun Wang
IJCAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We validate the method empirically, on multiple simulated datasets and one real-world dataset, and show that FST is two times faster than the four baselines while achieving a lower error rate under both Frobenius-norm and max-norm. |
| Researcher Affiliation | Academia | 1College of Software Engineering, Southeast University, China 2School of Computer Science and Engineering, Southeast University, China 3School of Artificial Intelligence, Southeast University, China 4Electrical Engineering and Automation, YOUPEI College, Yancheng Institute of Technology, China 5 School of Information Technology and Electrical Engineering, The University of Queensland, Australia 6 Department of Data Science & AI, Monash University, Australia |
| Pseudocode | Yes | The pseudocode of FST is shown in Algorithm 1 and the analysis of the computation complexity is proposed in Section 4.1. |
| Open Source Code | No | The paper does not provide an explicit statement about releasing open-source code for the described methodology or a link to a code repository. |
| Open Datasets | Yes | We also evaluate FST and baselines for estimating precision matrices on a real-world f MRI dataset: Social Brain [Tso et al., 2018]. |
| Dataset Splits | Yes | Tuning parameters selection. The tuning parameters of all the methods are selected through 5-fold cross-validation procedure. |
| Hardware Specification | Yes | Our experiment environment is a Linux server with E5-2630 v4 CPU and 64GB memories. All experiments are run using single core. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | Tuning parameters selection. The tuning parameters of all the methods are selected through 5-fold cross-validation procedure. For FST, we select λ from {0.1, 0.2, , 1.0} and ν from {0.1, 0.2 , 1.0}. Other configurations. Without special description, the Tν( ) we used in the following is the hard-thresholding. |