Implicit Relative Labeling-Importance Aware Multi-Label Metric Learning
Authors: Jun-Xiang Mao, Yong Rui, Min-Ling Zhang
AAAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Comprehensive experiments on benchmark multi-label datasets validate the superiority of our proposed approach in learning effective similarity metrics between multi-label examples. |
| Researcher Affiliation | Collaboration | 1School of Computer Science and Engineering, Southeast University, Nanjing 210096, China ... EMAIL, ... EMAIL" and "4Lenovo Research, Lenovo Group Ltd., Beijing, China ... EMAIL |
| Pseudocode | No | The complete procedure of the proposed ILIA approach is summarized in Appendix A. (Appendix A is not provided in the given text.) |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | Yes | In this paper, ten real-world multi-label datasets with diversified properties are employed for comparative studies. Table 1 summarizes the detailed characteristics of each benchmark dataset D, including the number of examples |D|, number of features dim(D), number of labels L(D), label cardinality LCard(D), and domain of datasets. 1 http://mulan.sourceforge.net/datasets.html 2 http://palm.seu.edu.cn/zhangml/Resources.htm#data |
| Dataset Splits | Yes | Ten-fold cross-validation is employed to evaluate the above compared approaches in this paper. |
| Hardware Specification | No | The paper does not provide specific hardware details used for running its 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 experiment. |
| Experiment Setup | Yes | For the proposed ILIA approach, we use the Polynomial kernel and set the parameters as follows: the trade-off parameters µ = 10 3, η = 10 2, γ = 10 2, and the number of nearest neighbors k = 20. ... For KNN and MLKNN, the number of nearest neighbors is fixed to 10 for fair comparisons. |