Label Enhancement for Label Distribution Learning
Authors: Ning Xu, An Tao, Xin Geng
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on one artificial dataset and fourteen real-world datasets show clear advantages of GLLE over several existing LE algorithms. |
| Researcher Affiliation | Academia | 1MOE Key Laboratory of Computer Network and Information Integration, China 2School of Computer Science and Engineering, Southeast University, Nanjing 210096, China 3School of Information Science and Engineering, Southeast University, Nanjing 210096, China |
| Pseudocode | No | The paper describes the algorithms using mathematical formulations and textual explanations but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide an unambiguous statement or link regarding the release of the source code for the described methodology. |
| Open Datasets | Yes | The second to the fourteen datasets are real-world LDL datasets [Geng, 2016] collected from biological experiments on the yeast genes, facial expression images, natural scene images and movies, respectively. http://cse.seu.edu.cn/Personal Page/xgeng/LDL/index.htm |
| Dataset Splits | Yes | Ten-fold cross validation is conducted for each algorithm. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU/CPU models or memory specifications used for running the experiments. |
| Software Dependencies | No | The paper mentions software components like 'BFGS' and 'SA-BFGS' but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | For GLLE, the parameter λ is chosen among {10 2, 10 1, ..., 100} and the number of neighbors K is set to c + 1. The kernel function in GLLE is Gaussian kernel. The parameter α in LP is set to 0.5. The number of neighbors K for ML is set to c + 1. The parameter β in FCM is set to 2. The kernel function in KM is Gaussian kernel. |