Robust Graph Dimensionality Reduction
Authors: Xiaofeng Zhu, Cong Lei, Hao Yu, Yonggang Li, Jiangzhang Gan, Shichao Zhang
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results indicated that our proposed method outperformed all the comparison methods in terms of different classification tasks. |
| Researcher Affiliation | Academia | Guangxi Key Lab of Multi-source Information Mining & Security, Guangxi Normal University, Guilin, 541004, China seanzhuxf@gmail.com, Cong L hu@163.com, yuhao.gxnu@qq.com, stublyg@163.com, 1960412020@qq.com, zhangscgxnu@gmail.com |
| Pseudocode | No | The paper describes the optimization algorithm in prose and mathematical equations but does not present it in a pseudocode block or algorithm format. |
| Open Source Code | No | The paper does not mention providing open-source code for the methodology described. |
| Open Datasets | No | We downloaded two binary-class datasets and two multi-class benchmark datasets from public website and listed their details in Table 1. (The paper mentions downloading data from a 'public website' and lists dataset names, but does not provide specific URLs, DOIs, repositories, or formal citations with authors/year for dataset access.) |
| Dataset Splits | Yes | During the training process, we used a 5-fold cross validation method to conduct model selection. |
| Hardware Specification | No | The paper does not specify any hardware details (e.g., CPU/GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions software like Support Vector Machine (SVM) but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | In model selection, we set parameters of all the comparison methods by following their corresponding literature and set the parameter λ in our method as {10 2, 10 1, . . . , 102}, and selected the parameters combination with the best performance for testing. |