Large-Margin Multi-Label Causal Feature Learning
Authors: Chang Xu, Dacheng Tao, Chao Xu
AAAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experimentations using synthetic and real-world data demonstrate that the proposed algorithm effectively discovers label causality, generates causal features, and improves multi-label learning. |
| Researcher Affiliation | Academia | Key Lab. of Machine Perception (Ministry of Education), Peking University, Beijing 100871, China Centre for Quantum Computation and Intelligent Systems, University of Technology, Sydney 2007, Australia |
| Pseudocode | No | The paper describes the optimization process using mathematical equations and text, but it does not include an explicitly labeled 'Pseudocode' or 'Algorithm' block. |
| Open Source Code | No | The paper does not contain any statements about releasing source code or providing links to a code repository for the methodology described. |
| Open Datasets | Yes | Six real-world datasets are used in our experiments. These datasets are extracted from diverse applications: Yahoo for web paper categorization, Enron for email analysis, Yeast for gene function prediction, and Scene, Image and Corel5K for image classification. All these datasets are obtained from the Mulan website. |
| Dataset Splits | Yes | For the LMCF algorithm, we set C2 = 0.1 and σ = 1, and determine the optimal γ and C1 on the validation sets. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU/CPU models, memory specifications) used for running the experiments. |
| Software Dependencies | No | The paper mentions a smoothing technique by Nesterov but does not specify any software libraries, frameworks, or programming language versions (e.g., Python, PyTorch, scikit-learn versions) used for implementation. |
| Experiment Setup | Yes | For the LMCF algorithm, we set C2 = 0.1 and σ = 1, and determine the optimal γ and C1 on the validation sets. |