Partial Multi-Label Learning via Large Margin Nearest Neighbour Embeddings
Authors: Xiuwen Gong, Dong Yuan, Wei Bao6729-6736
AAAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Comprehensive experiments on artificial and real-world datasets demonstrate the superiorities of the proposed PML-LMNNE. |
| Researcher Affiliation | Collaboration | Xiuwen Gong 1, 2, Dong Yuan 1, Wei Bao 1 1 Faculty of Engineering, The University of Sydney 2 Hunan Huishiwei Intelligent Technology Co., Ltd. |
| Pseudocode | Yes | Algorithm 1: PML-LMNNE Algorithm |
| Open Source Code | No | The paper does not provide any explicit statements or links indicating that the source code for the described methodology is open-source or publicly available. |
| Open Datasets | Yes | http://mulan.sourceforge.net/datasets-mlc.html |
| Dataset Splits | Yes | For PML-LMNNE, we initialize the regularization parameter C by 10-fold cross-validation over the range 10 2, . . . , 102 and use Euclidean metric to find the nearest neighbour in initializing the training process. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper mentions software like 'Libsvm', but does not specify version numbers for any key software components or libraries. |
| Experiment Setup | Yes | For PML-LMNNE, we initialize the regularization parameter C by 10-fold cross-validation over the range 10 2, . . . , 102 and use Euclidean metric to find the nearest neighbour in initializing the training process. |