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 | Conference PDF | Archive PDF | Plain Text | 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. yu-shengju@foxmail.com. Correspondence to: Siwei Wang <wangsiwei13@nudt.edu.cn>, Xinwang Liu <xinwangliu@nudt.edu.cn>. |
| 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. |