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].
Fair Incomplete Multi-View Clustering via Distribution Alignment
Authors: Qianqian Wang, Haiming Xu, Meiling Liu, Wei Feng, Xiangdong Zhang
IJCAI 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on three datasets containing sensitive features demonstrate that our method improves the fairness of clustering results while outperforming state-of-the-art IMVC methods in clustering performance. |
| Researcher Affiliation | Academia | 1School of Telecommunications Engineering, Xidian University, Xi an, China 2College of Information Engineering, Northwest A&F University, Yangling, China |
| Pseudocode | Yes | Algorithm 1 FIMVC-DA |
| Open Source Code | No | The paper does not contain any explicit statement about providing source code, nor does it include a link to a code repository. |
| Open Datasets | Yes | We evaluated our model on three fairness datasets: Credit Card, Zafar, and Bank [Zafar et al., 2017]. |
| Dataset Splits | No | The paper discusses 'Missing Rate' (0, 0.25, 0.5, 0.75 in Table 3) which refers to introduced missing views in the dataset, but it does not specify how the datasets were split into training, validation, or test sets for model development and evaluation. |
| Hardware Specification | No | Our experiments were all run on Windows 10 systems with Python 3.7 and Cuda 11.5. No specific hardware models (e.g., CPU, GPU) or their specifications are mentioned beyond the operating system. |
| Software Dependencies | Yes | Our experiments were all run on Windows 10 systems with Python 3.7 and Cuda 11.5. |
| Experiment Setup | Yes | The hidden layer dimension for all codec-related algorithms is set to 200 and every random seed has been set to 8. In all experiments, we set the learning rate to 0.0001. We chose Adam as the underlying optimizer. After training 500 batches. |