Structured Graph Learning Via Laplacian Spectral Constraints
Authors: Sandeep Kumar, Jiaxi Ying, Jose Vinicius de Miranda Cardoso, Daniel Palomar
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive numerical experiments with both synthetic and real data sets demonstrate the effectiveness of the proposed methods. An R package containing code for all the experimental results is available at https://cran.r-project. org/package=spectral Graph Topology. |
| Researcher Affiliation | Academia | Sandeep Kumar sandeep0kr@gmail.com Jiaxi Ying jx.ying@connect.ust.hk Jos e Vin ıcius de M. Cardoso jvdmc@connect.ust.hk Daniel P. Palomar , palomar@ust.hk Department of Industrial Engineering and Data Analytics Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong |
| Pseudocode | Yes | Algorithm 1, which we denote by SGL, summarizes the implementation of the structured graph learning via Laplacian spectral constraints. |
| Open Source Code | Yes | An R package containing code for all the experimental results is available at https://cran.r-project. org/package=spectral Graph Topology. [...] An R package containing the code for all the simulations is made available as open source repository. |
| Open Datasets | Yes | We apply SGL to learn similarity graphs from a real categorical animal dataset [65] with binary entries [...] Herein, animals data set [65, 67] is taken into consideration to learn weighted graphs. [...] We consider the RNA-Seq Cancer Genome Atlas Research Network [66] data set available at the UC-Irvine Machine Learning Database [68]. |
| Dataset Splits | No | The paper does not provide specific details about training, validation, or test dataset splits (e.g., percentages, sample counts, or methods for creating them). |
| Hardware Specification | No | The paper does not specify any hardware details (e.g., CPU, GPU models, or memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions an 'R package' for code but does not specify version numbers for R or any specific software libraries/dependencies used in the implementation. |
| Experiment Setup | Yes | Algorithm 1 Input: SCM S, k, c1, c2, β [...] The input for all the algorithms is the sample covariance matrix plus an identity matrix scaled by 1/3 (see [26]). [...] SGL with k = 1, and (d) SGL with k = 5. |