Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Robust Graph Dimensionality Reduction
Authors: Xiaofeng Zhu, Cong Lei, Hao Yu, Yonggang Li, Jiangzhang Gan, Shichao Zhang
IJCAI 2018 | Venue PDF | 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 EMAIL, Cong L EMAIL, EMAIL, EMAIL, EMAIL, EMAIL |
| 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. |