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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
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]. |