Dual Robust Unbiased Multi-View Clustering for Incomplete and Unpaired Information
Authors: Liang Zhao, Ziyue Wang, Chuanye He, Qingchen Zhang, Bo Xu
IJCAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on several challenging datasets demonstrate the superiority of our proposed method. We conduct extensive experiments on several commonly used real-world datasets under various scenarios, including PVP, PSP, and their simultaneous presence. The proposed method consistently achieves state-of-the-art performance. |
| Researcher Affiliation | Academia | Liang Zhao1 , Ziyue Wang1 , Chuanye He1 , Qingchen Zhang , 2 and Bo Xu1 1School of Software, Dalian University of Technology, Dalian, Liaoning, China 2School of Computer Science and Technology, Hainan University, Haikou, Hainan, China EMAIL, EMAIL, EMAIL |
| Pseudocode | No | The paper describes the proposed DRUMVC model using mathematical formulations and descriptive text, and illustrates the overall architecture in Figure 2. However, it does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any explicit statements about releasing source code, nor does it provide links to a code repository. |
| Open Datasets | Yes | In this study, we utilized four multi-view datasets: Scene15 [Fei-Fei and Perona, 2005], Reuters [Amini et al., 2009], Noisy MNIST [Wang et al., 2015], and MNIST-USPS [Peng et al., 2019]. |
| Dataset Splits | No | The paper mentions "an alignment rate of 0.5, a completeness rate of 0.5, and various scenarios of unaligned incomplete multi-view datasets" when describing experimental settings. However, it does not specify any training, validation, or test dataset splits, such as percentages or counts, that are necessary for reproducibility. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to run the experiments, such as GPU or CPU models. |
| Software Dependencies | No | The paper does not specify any software dependencies, libraries, or frameworks with version numbers that were used in the experiments. |
| Experiment Setup | No | The paper mentions hyperparameters like "β is a hyperparameter" and includes λ in the loss function, and refers to "Analysis of model parameters on Scene-15" and "various combinations of hyperparameters". However, it does not provide concrete values for these hyperparameters (e.g., learning rate, batch size, specific values for β or λ) or other training configurations within the main text. |