Adaptive Hypergraph Learning for Unsupervised Feature Selection
Authors: Xiaofeng Zhu, Yonghua Zhu, Shichao Zhang, Rongyao Hu, Wei He
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | 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. |