Robust Distance Metric Learning in the Presence of Label Noise
Authors: Dong Wang, Xiaoyang Tan
AAAI 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments on several UCI datasets and a real dataset with unknown noise patterns show that the proposed RNCA is more tolerant to class label noise compared to the original NCA method. |
| Researcher Affiliation | Academia | Dong Wang, Xiaoyang Tan Department of Computer Science and Technology Nanjing University of Aeronautics and Astronautics #29 Yudao Street, Nanjing 210016, P.R.China {dongwang, x.tan}@nuaa.edu.cn |
| Pseudocode | Yes | Algorithm 1 Robust Neighbourhood Components Analysis. |
| Open Source Code | No | The paper does not provide any links to source code or explicitly state that code for their method is open-source or publicly available. |
| Open Datasets | Yes | We use 6 datasets from the UCI database, with 3 multi-class datasets (i.e., Iris, balance and wine) and 3 datasets with binary labels (i.e., Heart, vote and ionosphere). |
| Dataset Splits | Yes | All the parameters involved in these methods are either chosen through 5 cross validation or using the default settings. |
| Hardware Specification | No | The paper does not specify any hardware details (e.g., GPU/CPU models, memory, cloud computing instances) used for running the experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow, or specific library versions). |
| Experiment Setup | No | The paper mentions that parameters are chosen through "5 cross validation or using the default settings" and discusses tuning parameters like λ and ε, but it does not provide specific hyperparameter values (e.g., learning rate, batch size, number of epochs) or detailed system-level training configurations used in their experiments. |