Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Reconstruction-based Unsupervised Feature Selection: An Embedded Approach
Authors: Jundong Li, Jiliang Tang, Huan Liu
IJCAI 2017 | Venue PDF | LLM Run Details | Input Tokens: 14,796 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,838 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on various types of realworld datasets demonstrate the effectiveness of the proposed framework REFS. |
| Researcher Affiliation | Academia | Computer Science and Engineering, Arizona State University, USA Computer Science and Engineering, Michigan State University, USA EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 Reconstruction-based Unsupervised Feature Selection (REFS) |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | Yes | We choose 8 benchmark datasets of various types for evaluation, including three image datasets, i.e., object image dataset COIL201 [Nene et al., 1996], face image dataset ORL2, handwritten digit datasets USPS [Hull, 1994]; two text datasets, i.e., RELATHE and BASEHOCK3; two microarray datasets, i.e., Lung [Bhattacharjee et al., 2001] and GLIOMA [Nutt et al., 2003] and one spoken letter recognition dataset Isolet4. Footnotes 1, 2, 3, 4 provide URLs for access. |
| Dataset Splits | No | The paper does not provide specific dataset split information (train/validation/test) for reproducing the data partitioning or the clustering experiments. It mentions using K-means for evaluation and varying the number of selected features. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU, GPU models, or memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions K-means and Lasso but does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | For Lap Score, MCFS, UDFS and FSASL, we specify the number of neighborhood size to be 5 to construct the Laplacian matrix on the data instances following previous work. In REFS, we also set the number nearest neighborhood size p to be 5, but the Laplacian matrix is built on the feature vectors instead of on the data instances. ... tune these regularization parameters for all methods by grid search and report the best performance. |