Partial Label Learning with Unlabeled Data
Authors: Qian-Wei Wang, Yu-Feng Li, Zhi-Hua Zhou
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on real-world data sets clearly validate the effectiveness of the proposed SSPL method. Extensive experiments on real-world partial label data sets clearly show that SSPL achieves highly competitive performance against state-of-the-art approaches. |
| Researcher Affiliation | Academia | Qian-Wei Wang , Yu-Feng Li and Zhi-Hua Zhou National Key Laboratory for Novel Software Technology, Nanjing University |
| Pseudocode | Yes | Algorithm 1 A simple solution; Algorithm 2 WGC (weighted graph construction) procedure; Algorithm 3 The proposed SSPL approach |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code or include links to code repositories for the methodology described. |
| Open Datasets | Yes | Three data sets are adopted for this task: Lost [Cour et al., 2011], Soccer Player [Zeng et al., 2013] and Yahoo!News [Guillaumin et al., 2010], MSRCv2 [Liu and Dietterich, 2012] data set is adopted for this task. The Bird Song [Briggs et al., 2012] data set is adopted for this task. Table 4: Characteristic of the real-world partial label data sets. |
| Dataset Splits | Yes | For each data set, we consider the percentage of partial label examples in the whole training set by randomly sampling p {0.05, 0.10, 0.15, 0.20, 0.30, 0.40, 0.50} instances from the whole training set with their candidate label sets and the other with no labeling information. In the rest of this section, five-fold cross-validation is performed on each real-world data set and in each training fold, the partial label instances are randomly sampled for three times. |
| Hardware Specification | No | The paper does not mention any specific hardware components (e.g., GPU/CPU models, memory, cloud instance types) used for running the experiments. |
| Software Dependencies | No | The paper mentions using specific algorithms and techniques like 'IPAL', 'PL-KNN', 'CLPL', and 'PL-SVM' but does not specify any software dependencies with version numbers (e.g., 'Python 3.x', 'PyTorch 1.x'). |
| Experiment Setup | Yes | parameters employed by SSPL are set as k = 10, α = 0.70, β = 0.25, r = 0.7 and T = 100. |