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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Adaptive Hypergraph Learning for Unsupervised Feature Selection
Authors: Xiaofeng Zhu, Yonghua Zhu, Shichao Zhang, Rongyao Hu, Wei He
IJCAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results show that our proposed method outperforms all the comparison methods in terms of clustering tasks. |
| Researcher Affiliation | Academia | 1 Guangxi Key Lab of Multi-source Information Mining & Security, China 2 Guangxi Normal University, China 3 Guangxi University, China |
| Pseudocode | No | The paper describes the optimization steps and equations but does not present them in a formal pseudocode or algorithm block. |
| Open Source Code | No | The paper does not provide any statement or link indicating the availability of open-source code for the described methodology. |
| Open Datasets | Yes | In this section, we evaluate our proposed AHLFS with the comparison methods in terms of the clustering accuracy of the clustering tasks, on eight public UCI datasets [Frank et al., 2010], whose detail is listed in Table 1. |
| Dataset Splits | No | The paper mentions selecting features (e.g., {20%, ..., 80%}) and repeating k-means clustering 20 times for average results, but does not specify a training/validation/test split for the datasets themselves for model validation. |
| Hardware Specification | No | The paper does not provide any specific hardware details used for running the experiments. |
| Software Dependencies | No | The paper does not specify any software names with version numbers. |
| Experiment Setup | Yes | In our experiments, we set the parameters range as {10^3, 10^2, ..., 10^3} where all the methods can achieve their best results. Our objective function has two tuning parameters, i.e., α and β. |