Partial Multi-View Clustering via Self-Supervised Network
Authors: Wei Feng, Guoshuai Sheng, Qianqian Wang, Quanxue Gao, Zhiqiang Tao, Bo Dong
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on several benchmark datasets show that the proposed PVC-SCN method outperforms several state-of-the-art clustering methods. |
| Researcher Affiliation | Academia | School of Computer Science and Technology, Xi an Jiaotong University, Xi an, Shaanxi, China, 710049 School of Telecommunications Engineering, Xidian University, Xi an, Shaanxi, China, 710071 School of Information, Rochester Institute of Technology, Rochester, NY, USA, 14623 School of Continuing Education, Xi an Jiaotong University, Xi an, Shaanxi, China, 710049 |
| Pseudocode | Yes | Algorithm 1: PVC-SSN |
| Open Source Code | No | The paper does not include an unambiguous statement about releasing the source code for the described methodology, nor does it provide a direct link to a code repository. |
| Open Datasets | Yes | To evaluate the effectiveness of the proposed PVC-SSN method, we conduct experiments on three benchmark datasets, i.e, BDGP (Cai et al. 2012), MNIST (Le Cun 1998) and HW (van Breukelen et al. 1998). |
| Dataset Splits | No | MNIST collects 70000 handwritten digits from 0 to 9, which are divided into training set and testing set. In our experiment, only the training set of 4000 handwritten digits is used. |
| Hardware Specification | Yes | All the experiments are run on the platform of Ubuntu Linux 16.04 with NVIDIA Titan Xp Graphics Processing Units (GPUs) and 64 GB memory size. |
| Software Dependencies | No | The paper mentions using 'Py Torch' and 'MATLAB' but does not specify their version numbers, nor does it list specific versioned libraries or solvers. |
| Experiment Setup | Yes | Moreover, we use Adam (Kingma and Ba 2014) optimizer with the learning rate of 0.0001 and other default settings to train our model. We conduct the convergence experiments with 2000 epochs on BDGP dataset and with 1000 epochs on MNIST dataset and HW dataset. |