Double-Layer Hybrid-Label Identification Feature Selection for Multi-View Multi-Label Learning
Authors: Pingting Hao, Kunpeng Liu, Wanfu Gao
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on six datasets demonstrate the effectiveness and superiority of our method compared with the state-of-the-art methods. Experiments Experimental Setup Datasets. Following (Zhu, Li, and Zhang 2015; Zhang et al. 2020b; Li and Chen 2021), six popular multiview multi-label datasets are adopted to facilitate a fair result comparison with state-of-the-art methods, including SCENE, OBJECT, MIRFlickr, Corel5K, IAPRTC12 and 3Sources. |
| Researcher Affiliation | Academia | 1 College of Computer Science and Technology, Jilin University, China 2 Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, China 3 Department of Computer Science, Portland State University, Portland, OR 97201 USA |
| Pseudocode | Yes | Algorithm 1: Double-layer Hybrid-Label Identification |
| Open Source Code | No | The paper does not include an unambiguous statement about releasing code or a direct link to a source-code repository for the described methodology. |
| Open Datasets | Yes | Datasets. Following (Zhu, Li, and Zhang 2015; Zhang et al. 2020b; Li and Chen 2021), six popular multiview multi-label datasets are adopted to facilitate a fair result comparison with state-of-the-art methods, including SCENE, OBJECT, MIRFlickr, Corel5K, IAPRTC12 and 3Sources. |
| Dataset Splits | Yes | On each dataset, five-fold cross-validation is performed and the mean accuracy, as well as standard deviation, are reported. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | The parameters of the regularization paradigm are searched in set {10 3, 10 2, ..., 103}, and four metrics commonly used in this field are selected to evaluate these methods. |