Label Distribution Learning by Exploiting Sample Correlations Locally
Authors: Xiang Zheng, Xiuyi Jia, Weiwei Li
AAAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results demonstrate that LDL-SCL can effectively deal with the label distribution problems and perform remarkably better than the state-of-the-art LDL methods. |
| Researcher Affiliation | Academia | Xiang Zheng, Xiuyi Jia School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing 210014, China Weiwei Li College of Astronautics Nanjing University of Aeronautics and Astronautics Nanjing 210016, China |
| Pseudocode | Yes | Algorithm 1: The LDL-SCL algorithm |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the methodology is openly available. |
| Open Datasets | Yes | The datasets used in the experiments were collected from biological experiments on the budding yeast Saccharomyces cerevisiae (Eisen et al. 1998). |
| Dataset Splits | Yes | For each dataset, 10 times 10-fold cross-validation are employed and average results are recorded. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to run the experiments, such as CPU or GPU models. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies or libraries used in the implementation. |
| Experiment Setup | Yes | For LDL-SCL, the parameters in Eq. (9) are set to: λ1 = 0.001, λ2 = 0.001 and λ3 = 0.001. |