Efficient Federated Multi-View Clustering with Integrated Matrix Factorization and K-Means
Authors: Wei Feng, Zhenwei Wu, Qianqian Wang, Bo Dong, Zhiqiang Tao, Quanxue Gao
IJCAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on multiple datasets demonstrate the effectiveness of FMVC-IMK. |
| Researcher Affiliation | Academia | 1School of Computer Science and Technology, Xi an Jiaotong University, Xi an, China 2School of Telecommunications Engineering, Xidian University, Xi an, China 3School of Continuing Education, Xi an Jiaotong University, Xi an, China 4School of Information, Rochester Institute of Technology, NY, USA |
| Pseudocode | Yes | Algorithm 1 FMVC-IMK |
| Open Source Code | No | The paper does not contain any statement about releasing open-source code or a link to a code repository for the described methodology. |
| Open Datasets | Yes | We evaluate our method on eight public multi-view datasets: (1)3-sources... (2)BBCSport [Greene and Cunningham, 2006]... (3)ORL [Samaria and Harter, 1994]... (4)Sonar [Sejnowski and Gorman, ]... (5)Yale... (6)Vehicle Sensor [Duarte and Hu, 2004]... (7)Human Activity Recognition(HAR) [Reyes-Ortiz and Parra, 2012]... (8)Sentences NYU v2(RGB-D) [Silberman et al., 2012]. |
| Dataset Splits | No | The paper does not provide specific details about training, validation, or test dataset splits (e.g., percentages or sample counts). |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments, such as GPU or CPU models. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library or solver names with version numbers. |
| Experiment Setup | Yes | (10) indicates that our objective function involves two hyperparameters: λ and µ, and Fig.3 depicts the ACC when λ and µ take values on the interval of [0.0001, 0.001, 0.01, 0.1, 1, 10] on four datasets. |