Online Metric Learning for Multi-Label Classification
Authors: Xiuwen Gong, Dong Yuan, Wei Bao4012-4019
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Comprehensive experiments on a number of benchmark multi-label datasets validate our theoretical approach and illustrate that our proposed online metric learning (OML) algorithm outperforms state-of-the-art methods. |
| Researcher Affiliation | Academia | Xiuwen Gong, Dong Yuan, Wei Bao Faculty of Engineering, The University of Sydney {xiuwen.gong, dong.yuan, wei.bao}@sydney.edu.au |
| Pseudocode | Yes | Algorithm 1 Online Metric Learning for Multi-Label Classification |
| Open Source Code | No | The paper states 'The codes are provided by the respective authors.' in the context of baseline methods, and provides a link to a dataset source, but there is no explicit statement or link for the open-source code of their own proposed methodology. |
| Open Datasets | Yes | To evaluate the performance of our proposed online metric learning algorithm, we conduct experiments on four benchmark datasets: Corel5k, Enron, Medical and Emotions. The statistics of these datasets can be found in website1. 1http://mulan.sourceforge.net |
| Dataset Splits | No | The paper states 'Initially, we keep 20% of data for nearest neighbor searching.' but does not provide specific train/validation/test dataset splits needed to reproduce the experiment. |
| Hardware Specification | Yes | All experiments are conducted on a workstation with 3.20GHz Intel CPU and 16GB main memory, running the Windows 10 platform. |
| Software Dependencies | No | The paper does not provide specific software names with version numbers (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | In our experiment, the matrix V1 is initialized as a normal distributed random matrix. Initially, we keep 20% of data for nearest neighbor searching. In our experiment, M is set to 100000 and m is set to 0.00001, while k is set to 10. Parameter λ in OLANSGD is chosen from among {10 6, 10 5, , 100} using five-fold cross validation. We use the default parameter for OSML-ELM. |