Efficient Multi-view Clustering via Reinforcement Contrastive Learning
Authors: Qianqian Wang, Haiming Xu, Zihao Zhang, Zhiqiang Tao, Quanxue Gao
IJCAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experimental results showcase the effectiveness and superiority of our proposed method on multiple benchmark datasets. ... 4 Experiment 4.1 Datasets & Metric 4.2 Experiment Setup 4.3 Performance Comparison 4.4 Hyper-parameter Analysis 4.5 Ablation Studies 4.6 Visualization Analysis 4.7 Convergence Analysis |
| Researcher Affiliation | Academia | 1School of Telecommunications Engineering, Xidian University, Xi an, China 2Department of Computer Science, Rochester Institute of Technology, Rochester, NY, USA |
| Pseudocode | No | The paper describes the methodology in prose and mathematical formulations within section 3, but does not present any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper states 'implemented using Py Torch (Python 3.10.13)' but does not provide any explicit statement about releasing the source code or a link to a repository for the methodology described. |
| Open Datasets | Yes | We evaluate our method on six real-world multi-view datasets from previous works [Chen et al., 2023; Xu et al., 2023]: COIL-20 with 1,440 grayscale images of 20 objects from different angles, RGB-D containing 1,449 samples with RGB and depth information, BDGP consisting of 2,500 drosophila embryo images with two visual descriptors, Scene-15 including 4,485 scene images with GIST, LBP, and HOG features, MNIST-USPS combining handwritten digit samples from both datasets, and Fashion containing product images with visual, textual, and categorical features. |
| Dataset Splits | No | The paper lists the datasets used and evaluation metrics, but it does not explicitly provide details about how these datasets were split into training, test, or validation sets for reproducibility. |
| Hardware Specification | Yes | All experiments were conducted on an NVIDIA Ge Force RTX 4090 GPU with CUDA 12.4, implemented using Py Torch (Python 3.10.13). |
| Software Dependencies | Yes | All experiments were conducted on an NVIDIA Ge Force RTX 4090 GPU with CUDA 12.4, implemented using Py Torch (Python 3.10.13). |
| Experiment Setup | No | The paper discusses the sensitivity of hyperparameters λ and β and mentions other parameters like αt, τ1, αc, ηe, ηc, τ, and γ in the methodology. However, it does not provide a specific set of values for these hyperparameters that were used to achieve the reported experimental results in the performance comparison table. |