Label Enhancement with Sample Correlations via Low-Rank Representation
Authors: Haoyu Tang, Jihua Zhu, Qinghai Zheng, Jun Wang, Shanmin Pang, Zhongyu Li5932-5939
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on 14 datasets demonstrate that the algorithm accomplishes stateof-the-art results as compared to previous label enhancement baselines. |
| Researcher Affiliation | Academia | 1School of Software Engineering, Xi an Jiaotong University, Xian 710049, China 2Shanghai Institute for Advanced Communication and Data Science, School of Communication and Information Engineering,Shanghai University, Shanghai 200444, China tanghao258@stu.xjtu.edu.cn, zhujh@xjtu.edu.cn |
| Pseudocode | No | The paper describes the algorithm using mathematical formulations and descriptive text, but does not include a formally structured pseudocode or algorithm block. |
| Open Source Code | No | The paper does not provide any explicit statements about open-sourcing the code or links to a code repository for the described methodology. |
| Open Datasets | Yes | The last datasets from Yeast-alpha to Yeast-spoem are collected from the records of 10 biological experiments on the budding yeast genes(Eisen et al. 1998). The artificial dataset was also adopted in (Xu, Tao, and Geng 2018). |
| Dataset Splits | Yes | In particular, for a given dataset, the ten-fold cross validation was executed. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper describes mathematical methods and algorithms used (e.g., L-BFGS, ALM) but does not list any specific software dependencies or libraries with version numbers (e.g., Python, PyTorch, scikit-learn versions). |
| Experiment Setup | Yes | The parameters λ1 and λ2 are selected among {0.0001, 0.001, ..., 10} in our LESC algorithm. As for GLLE, the number of neighbors K is set to c + 1 and the parameters λ are set among {0.01, 0.1, ..., 100}. We also choose the parameter α in LP to be 0.5, the number of neighbors K for ML to be c + 1, and the parameter β in FCM to be 2. |