Label Specific Multi-Semantics Metric Learning for Multi-Label Classification: Global Consideration Helps
Authors: Jun-Xiang Mao, Wei Wang, Min-Ling Zhang
IJCAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on benchmark multi-label data sets validate the superiority of our proposed approach in learning effective distance metrics for multi-label classification. |
| Researcher Affiliation | Academia | 1School of Computer Science and Engineering, Southeast University, Nanjing 210096, China 2Key Laboratory of Computer Network and Information Integration (Southeast University), Ministry of Education, China 3The University of Tokyo, Japan |
| Pseudocode | Yes | Algorithm 1 The pseudo-code of LIMIC. |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the described methodology (LIMIC) is publicly available. |
| Open Datasets | Yes | In this paper, eight benchmark multi-label data sets have been employed for comprehensive performance evaluation. Table 1 summarizes the characteristics of each experimental data set S... 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 approaches on the 8 benchmark multi-label data sets. |
| Hardware Specification | No | The paper mentions "We thank the Big Data Center of Southeast University for providing the facility support on the numerical calculations in this paper." but does not specify any hardware details such as CPU/GPU models or memory. |
| Software Dependencies | No | The paper does not explicitly list specific software dependencies with their version numbers required to replicate the experiments. |
| Experiment Setup | Yes | For the proposed LIMIC approach, regularization parameters λ1 and λ2 are searched in {10 3, 10 2, . . . , 103}. The number of targets and imposters is fixed to 10 and γ in Eq.(8) is set to 2 which is consistent with conventional metric learning approaches [Weinberger and Saul, 2009; Ye et al., 2020]. |