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..
Towards Resource-friendly, Extensible and Stable Incomplete Multi-view Clustering
Authors: Shengju Yu, Zhibin Dong, Siwei Wang, Xinhang Wan, Yue Liu, Weixuan Liang, Pei Zhang, Wenxuan Tu, Xinwang Liu
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Many results suggest that To RES exhibits advantages against 20 SOTA algorithms, even in scenarios with a higher ratio of incomplete data. |
| Researcher Affiliation | Academia | 1School of Computer, National University of Defense Technology, Changsha, Hunan, China 2Intelligent Game and Decision Lab, Beijing, China. EMAIL. Correspondence to: Siwei Wang <EMAIL>, Xinwang Liu <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 Scheme 1 for solving Eq. (4) Algorithm 2 Scheme 2 for solving Eq. (4) Algorithm 3 To RES |
| Open Source Code | No | The paper does not contain an explicit statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | Six widely-used datasets are adopted in experiments, including small-scale datasets: Webkb, Wikifea; middle-scale datasets: AWA10, SUNRGB-D; large-scale datasets: Aw Afea, EMNIST. Table 3 describes their details. ... H. The URLs of Benchmark Datasets ... Webkb: https://www.cs.cmu.edu/ webkb/ Wikifea: http://svcl.ucsd.edu/projects/crossmodal/ AWA10: https://cvml.ista.ac.at/Aw A2/ SUNRGB-D: https://rgbd.cs.princeton.edu/ Aw Afea: https://cvml.ista.ac.at/Aw A/ EMNIST: https://www.nist.gov/itl/products-and-services/emnist-dataset |
| Dataset Splits | No | The paper mentions evaluating under different "percentage of incomplete data (PID)" (20%, 40%, 60%) but does not provide specific train/validation/test dataset splits, percentages, or sample counts. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to run the experiments, such as CPU models, GPU models, or memory specifications. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions, or other libraries/solvers). |
| Experiment Setup | No | The paper mentions its method aims to "eliminate hyper-parameters" but does not provide specific hyperparameter values or detailed system-level training settings for its own method or the baselines compared. There is no dedicated section or table detailing the experimental setup with concrete configuration parameters. |