Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
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 | Venue PDF | 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 EMAIL, EMAIL |
| 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. |