Limited-Supervised Multi-Label Learning with Dependency Noise
Authors: Yejiang Wang, Yuhai Zhao, Zhengkui Wang, Wen Shan, Xingwei Wang
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments conducted on various datasets demonstrate the superiority of our proposed method. Experiments Experimental Setting Datasets. We conduct the experiments on 10 datasets including slashdot, medical, enron, scene, yeast, 20ng, corel5k, mirflickr, eurlex_dc and m_emotion (abbr. of music_emotion) (Zhang and Zhou 2013; Trohidis et al. 2008). |
| Researcher Affiliation | Academia | Yejiang Wang1, Yuhai Zhao1*, Zhengkui Wang2, Wen Shan3, Xingwei Wang1 1School of Computer Science and Engineering, Northeastern University, China 2Info Comm Technology Cluster, Singapore Institute of Technology, Singapore 3Singapore University of Social Sciences, Singapore |
| Pseudocode | Yes | Algorithm 1: MLDN Input: train data X, assigned label matrix Υ Output: weight matrix W 1: ω 1 and initialize W, Q, P 2: while not converged 3: Update W using Eq.(Z ) and Eq.(W ); 4: ω 1/2 + 4 ω2 + 1/2; 5: W W, W W and ω ω; 6: Update Q using Eq.(Q ); 7: Update P using Eq.(P ); 8: return W |
| Open Source Code | No | The paper does not provide any explicit statements about making its source code publicly available or links to a code repository. |
| Open Datasets | Yes | Datasets. We conduct the experiments on 10 datasets including slashdot, medical, enron, scene, yeast, 20ng, corel5k, mirflickr, eurlex_dc and m_emotion (abbr. of music_emotion) (Zhang and Zhou 2013; Trohidis et al. 2008). |
| Dataset Splits | Yes | Experimental Setup We use ten-fold cross-validation with a training/test set ratio of 8:2. |
| Hardware Specification | No | The paper discusses running time analysis but does not specify any particular hardware components (e.g., CPU, GPU models, or memory) used for conducting the experiments. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software libraries, frameworks, or programming languages used in the experiments. |
| Experiment Setup | Yes | In this experiment, we conduct the sensitivity analysis for our method on the yeast data set over the parameters including α, β, γ and λ. We choose them from {10^-3, ..., 10^2, 10^3}. The convergence condition is determined when the loss difference between the two iterations is less than 10^-3. |