Partial Label Learning with Self-Guided Retraining
Authors: Lei Feng, Bo An3542-3549
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on synthesized and real-world datasets demonstrate that the proposed approach significantly outperforms the state-of-the-art partial label learning approaches. |
| Researcher Affiliation | Collaboration | 1School of Computer Science and Engineering, Nanyang Technological University, Singapore 2Alibaba-NTU Singapore Joint Research Institute, Singapore |
| Pseudocode | Yes | The pseudo code of SURE is presented in Algorithm 1. |
| Open Source Code | No | The paper does not provide an explicit statement about the release of source code for the methodology described, nor does it include a link to a code repository. |
| Open Datasets | Yes | Following the widely-used controlling protocol (Cour, Sapp, and Taskar 2011; Liu and Dietterich 2012; Zhang and Yu 2015; Wu and Zhang 2018; Feng and An 2018; Wang and Zhang 2018), each UCI dataset can be used to generate artificial partial label datasets. These datasets are publicly available at: http://cse.seu.edu.cn/Personal Page/zhangml/ |
| Dataset Splits | Yes | Parameters for each algorithm are selected by five-fold cross-validation on the training set. For each dataset, ten-fold cross-validation is performed where mean prediction accuracies and the standard deviations are recorded. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU/GPU models, memory, or cloud instance types) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library names with version numbers, needed to replicate the experiment. |
| Experiment Setup | Yes | The two parameters λ and β for SURE are chosen from {0.001, 0.01, 0.05, 0.1, 0.3, 0.5, 1}. Parameters for each algorithm are selected by five-fold cross-validation on the training set. In this paper, Gaussian kernel function κ(xi, xj) = exp( xi xj 2 2 /(2σ2)) is employed with σ set to the averaged pairwise distances of instances. |