Pseudo-Label Reconstruction for Partial Multi-Label Learning
Authors: Yu Chen, Fang Li, Na Han, Guanbin Li, Hongbo Gao, Sixian Chan, Xiaozhao Fang
IJCAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments and analyses demonstrate that the proposed PML-PLR outperforms state-of-the-art methods. ... 4 Experiments 4.1 Datasets 4.2 Baselines and Implementation Details 4.3 Experimental Results 4.4 Further Analysis |
| Researcher Affiliation | Academia | 1Guangdong University of Technology 2Guangdong Polytechnic Normal University 3Sun Yat-sen University 4Institute of Advanced Technology, University of Science and Technology of China 5Zhejiang University Of Technology |
| Pseudocode | No | The paper describes the optimization steps in Section 3.6 'Optimization' using mathematical formulas but does not contain a structured pseudocode or algorithm block. |
| Open Source Code | No | The paper does not provide an explicit statement about the release of source code for the methodology described, nor does it include any links to code repositories. |
| Open Datasets | Yes | To evaluate the generalization performance of our proposed PML-PLR method, a total of 25 datasets are used for comparative study. ... 1http://palm.seu.edu.cn/zhangml/ 2http://mulan.sourceforge.net/datasets.html |
| Dataset Splits | No | The paper states, 'Cross-validation is employed to select the optimal latent space dimension m.' for parameter selection, but it does not provide specific details on how the datasets were split (e.g., percentages or k-fold setup) for the main experimental evaluations presented in the results tables. |
| Hardware Specification | No | The paper does not provide any specific hardware details (such as GPU/CPU models, memory, or specific computing environments) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiments. |
| Experiment Setup | Yes | The parameters α, β and λ in the PML-PLR are selected using grid search from {10 4, 10 3, 10 2, 10 1, 100}. |