Incomplete Multi-view Clustering via Prototype-based Imputation
Authors: Haobin Li, Yunfan Li, Mouxing Yang, Peng Hu, Dezhong Peng, Xi Peng
IJCAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments demonstrate the superiority of our method on five challenging benchmarks compared with 11 approaches.In this section, we evaluate the proposed Pro Imp method on five widely-used multi-view datasets compared with 11 baselines. |
| Researcher Affiliation | Academia | College of Computer Science, Sichuan University {haobinli.gm, yunfanli.gm, yangmouxing, penghu.ml}@gmail.com, pengdz@scu.edu.cn, pengx.gm@gmail.com |
| Pseudocode | No | The paper describes the proposed method using figures and mathematical equations but does not include explicit pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code could be accessed from https://pengxi.me. |
| Open Datasets | Yes | Five multi-view datasets are used in our experiments, including Scene15 [Fei-Fei and Perona, 2005], Reuters [Amini et al., 2009], Noisy MNIST [Wang et al., 2015], CUB [Zhang et al., 2019a], and MNIST-USPS [Peng et al., 2019]. |
| Dataset Splits | No | The paper does not explicitly mention a 'validation' dataset split or its size/percentage for reproducing experiments. |
| Hardware Specification | Yes | The proposed Pro Imp is implemented in Py Torch 1.11.0 and all the experiments are conducted on an NVIDIA 3090 GPU on Ubuntu 20.04 OS. |
| Software Dependencies | Yes | The proposed Pro Imp is implemented in Py Torch 1.11.0 and all the experiments are conducted on an NVIDIA 3090 GPU on Ubuntu 20.04 OS. |
| Experiment Setup | Yes | The model is trained for 150 epochs using the Adam optimizer with an initial learning rate of 1e-3, with a batch size of 1,024 on all datasets. The similarity bound α in Eq. 6 and the weight parameter in Eq. 7 are set to 0.75 and 0.02, respectively. |