Federated Multi-View Clustering via Tensor Factorization
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 | Extensive experiments on several datasets demonstrate that our proposed method exhibits superior performance in federated multi-view clustering. |
| 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 Tensor FMVC |
| Open Source Code | No | The paper does not provide any statement or link indicating the release of open-source code for the described methodology. |
| Open Datasets | Yes | Datasets: We evaluate our method on eight public multi-view datasets: (1)3-sources is a three-view text dataset with 169 samples sourced from three reputable news outlets. (2)BBCSport [Greene and Cunningham, 2006] is composed of 544 sports news articles sourced from the BBC Sport website and is categorized into five distinct topical areas. It has two views and the dimensions are 3283 and 3183. (3)ORL [Samaria and Harter, 1994] is a three-view dataset of 400 facial images, categorized into 40 classes. (4)Sonar [Sejnowski and Gorman, ] includes three views and extracts its multi-view features from 208 patterns. (5)Yale is a two-view dataset of 165 facial images of 11 people. (6)Vehicle Sensor((VS) [Duarte and Hu, 2004] is a four-view dataset whose features are gathered from distributed sensors. (7)Human Activity Recognition(HAR) [Reyes-Ortiz and Parra, 2012] is a four-view dataset with 10299 samples that documents six daily activities performed; (8)Sentences NYU v2(RGBD) [Silberman et al., 2012] includes images of indoor scenes and corresponding descriptions. |
| Dataset Splits | No | The paper mentions evaluating methods on several multi-view datasets but does not provide specific details on how these datasets were split into training, validation, and test sets, nor does it specify cross-validation methods. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU/CPU models, memory, or cloud computing instance types used for running the experiments. |
| Software Dependencies | No | The paper does not list any specific software dependencies with version numbers (e.g., programming languages, libraries, or frameworks) required to reproduce the experiments. |
| Experiment Setup | Yes | Parameter analysis: There are three parameters in Eq. (10), i.e., λ, p, and r, and one parameter Ωin Eq. (16). We study the influence of their values on the clustering performance, whose results are illustrated in Fig. 3. We conclude that: (1) Tensor FMVC achieves relatively poor performance when the λ is too small or too large... The suggested range for λ is [10, 100]. (2) A larger r represents the smaller difference in client contributions... The suggested range of r is 5 to 7. (3) p s value greatly influences the ACC... (4) The ACC curve w.r.t. Ωillustrates that the clustering performance deteriorates when Ωis too large or too small. Hence, the recommended value of Ωis 0.01. |