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].
Sample-Level Cross-View Similarity Learning for Incomplete Multi-View Clustering
Authors: Suyuan Liu, Junpu Zhang, Yi Wen, Xihong Yang, Siwei Wang, Yi Zhang, En Zhu, Chang Tang, Long Zhao, Xinwang Liu
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on six benchmark datasets demonstrate the ability of SCSL in processing incomplete multi-view clustering tasks. Our code is publicly available at https://github.com/Tracesource/SCSL. Experiments Experimental Settings Datasets We evaluate the effectiveness of the proposed algorithm using six widely used datasets: MSRCV, ORL, Protein Fold, Wiki, CCV, and SUNRGBD. |
| Researcher Affiliation | Academia | 1 School of Computer, National University of Defense Technology, Changsha, China, 410073 2 Intelligent Game and Decision Lab, Beijing, China, 100091 3 School of Computer Science, China University of Geosciences, Wuhan, China, 430074 4 Shandong Computer Science Center, Qilu University of Technology, Jinan, China, 250000 |
| Pseudocode | Yes | Algorithm 1: SCSL |
| Open Source Code | Yes | Our code is publicly available at https://github.com/Tracesource/SCSL. |
| Open Datasets | Yes | We evaluate the effectiveness of the proposed algorithm using six widely used datasets: MSRCV, ORL, Protein Fold, Wiki, CCV, and SUNRGBD. |
| Dataset Splits | No | The paper mentions generating incomplete datasets and searching for hyperparameters but does not provide specific details on training, validation, or test splits (e.g., percentages or counts). |
| Hardware Specification | Yes | All experiments were conducted on a desktop computer equipped with an Intel Core i9-10900X CPU, 64GB of RAM, and MATLAB 2020b (64-bit). |
| Software Dependencies | Yes | All experiments were conducted on a desktop computer equipped with an Intel Core i9-10900X CPU, 64GB of RAM, and MATLAB 2020b (64-bit). |
| Experiment Setup | Yes | For all the aforementioned algorithms, we configured their parameters within their recommended ranges. In our proposed method, we search β in [0.001, 1, 10] and λ in [0.001, 0.1, 1]. |