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].
Imputation-free Incomplete Multi-view Clustering via Knowledge Distillation
Authors: Benyu Wu, Wei Du, Jun Wang, Guoxian Yu
IJCAI 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on benchmark datasets demonstrate the effectiveness of I2MVC. Experiments on benchmark datasets demonstrate the effectiveness of I2MVC. |
| Researcher Affiliation | Academia | Benyu Wu , Wei Du , Jun Wang and Guoxian Yu School of Software, Shandong University, Jinan, China EMAIL, EMAIL. All authors are affiliated with Shandong University, an academic institution, and their email domains (163.com and sdu.edu.cn) are consistent with academic affiliations. |
| Pseudocode | No | The paper describes the methodology in prose and through diagrams (Figure 2) but does not include any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper states, 'We used the publicly available code for the compared methods and followed the implementation details provided in their original papers.' However, it does not explicitly state that the code for I2MVC, the methodology described in this paper, is publicly available or provide a link to its repository. |
| Open Datasets | Yes | The datasets used in this study are benchmark datasets widely used in MVC, including NGs [Hussain et al., 2010], BDGP [Cai et al., 2012], Web KB [Craven et al., 1998], and MNIST-USPS [Peng et al., 2019]. |
| Dataset Splits | No | The paper describes how incomplete multi-view data (MVD) was constructed through random masking, but it does not specify explicit training, validation, or test dataset splits for the experiments. It mentions 'Each phase was trained for at least 50 iterations' but does not provide details on how the datasets were partitioned for these phases. |
| Hardware Specification | Yes | The code of I2MVC was developed using Python 3.8 and Py Torch 1.13, and all experiments were conducted on a Tesla T4 GPU with 16GB memory. |
| Software Dependencies | Yes | The code of I2MVC was developed using Python 3.8 and Py Torch 1.13, and all experiments were conducted on a Tesla T4 GPU with 16GB memory. |
| Experiment Setup | Yes | The distillation temperature τ2 was set to 2, and the view missing rate varied within {0, 0.1, 0.3, 0.5, 0.7}. The learning rate for the Teacher model was set to 3e-4 during both the pre-training and training phases, while the Student model in the distillation phase used a learning rate of 3e-5. Each phase was trained for at least 50 iterations. The hyper-parameters α, β were searched within {0.001, 0.01, 0.1, 1, 10} and ranged 0.1 0.9 with step 0.1, respectively. |