Nonconvex Sparse Graph Learning under Laplacian Constrained Graphical Model
Authors: Jiaxi Ying, José Vinícius de Miranda Cardoso , Daniel Palomar
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical experiments involving synthetic and realworld data sets demonstrate the effectiveness of the proposed method. |
| Researcher Affiliation | Academia | Jiaxi Ying1 jx.ying@connect.ust.hk José Vinícius de M. Cardoso1 jvdmc@connect.ust.hk Daniel P. Palomar1,2 palomar@ust.hk Department of Electronic and Computer Engineering1 Department of Industrial Engineering and Decision Analytics2 The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong |
| Pseudocode | Yes | Algorithm 1 Nonconvex Graph Learning (NGL) |
| Open Source Code | Yes | An open source R package is available at https://github.com/mirca/sparse Graph. |
| Open Datasets | Yes | We conduct numerical experiments on the 2019-n Co V data set1 from 98 anonymous Chinese patients affected by the outbreak of 2019-n Co V on early February, 2020. [...] 2019-n Co V data is available in a queryable format via the R package n Cov2019 which lives on Git Hub: https://github.com/Guangchuang Yu/n Cov2019. |
| Dataset Splits | No | The paper generates synthetic data or uses a real-world dataset to form a sample covariance matrix (S) for learning the graph. It does not explicitly mention splitting the data into distinct training, validation, and test sets for model development and evaluation. |
| Hardware Specification | Yes | The computational time for GLE-ADMM, NGL-SCAD and NGL-MCP are 2.9, 0.7 and 0.8 seconds, respectively, conducted on a PC with a 2.8 GHz Inter Core i7 CPU and 16 GB RAM. |
| Software Dependencies | No | The paper mentions 'An open source R package' and 'R package n Cov2019' but does not specify version numbers for R itself, these packages, or any other software dependencies. |
| Experiment Setup | Yes | We set γ equal to 1.01 in h MCP,λ(x) and 2.01 in h SCAD,λ(x) for all the experiments. [...] The number of nodes in the Barabasi-Albert graph is p = 50, and the weights associated with edges are uniformly sampled from U(2, 5). The sample covariance matrix is constructed by S = 1/n XX, where n is the number of samples. [...] The sample size ratio is n/p = 100. [...] The regularization parameter λ for each algorithm is fine-tuned. |