Scalable Federated One-Step Multi-View Clustering with Tensorized Regularization
Authors: Wei Feng, Danting Liu, Qianqian Wang, Wenqi Liang, Zheng Yan
AAAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on multiple datasets validate the privacy and effectiveness of our method. |
| Researcher Affiliation | Academia | Wei Feng1, Danting Liu1, Qianqian Wang2*, Wenqi Liang3, Zheng Yan4 1School of Computer Science and Technology, Xi an Jiaotong University 2School of Telecommunications Engineering, Xidian University 3University of the Chinese Academy of Sciences 4School of Cyber Engineering, Xidian University EMAIL, EMAIL,EMAIL, EMAIL,EMAIL |
| Pseudocode | Yes | Algorithm 1: SFOMVC-TR |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. There are no explicit statements about code release, repository links, or mentions of code in supplementary materials. |
| Open Datasets | Yes | We evaluate our method on eight public multiview datasets: (1)ORL (Samaria and Harter 1994); (2)Yale; (3)BBCSport (Greene and Cunningham 2006); (4)3sources; (5)Web KB (Blum and Mitchell 1998); (6) Uci Digit; (7)NGs (Hussain, Bisson, and Grimal 2010); (8) Reuters (Apt e, Damerau, and Weiss 1994). |
| Dataset Splits | No | The paper mentions the use of eight public multi-view datasets but does not specify the training, validation, or test split percentages, sample counts, or refer to standard predefined splits with citations for reproducibility. It only states: "Our experiments include a server and multiple clients, each holding a view of the data." |
| Hardware Specification | No | The paper does not provide specific hardware details such as exact GPU/CPU models, processor types with speeds, or memory amounts used for running its experiments. It discusses computational efficiency and scalability but without explicit hardware specifications. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers. It describes the algorithms and methods but does not list programming languages, libraries, or solvers with their respective versions. |
| Experiment Setup | Yes | Our objective function involves two penalty parameters, i.e., β and γ. Figure 3 illustrates the ACC with regard to (w.r.t) β and γ on two datasets. We also test the performance w.r.t. the three hyperparameters: anchor rate r, parameter p of the Schatten p-norm, and q of the Lp,q-norm. Each client Cm respectively optimizes the local objective in Eq. (1) with the assigned weight αm via the Augmented Lagrange Multiplier (ALM) method. Algorithm 1: SFOMVC-TR ... 2: Each client Cm initializes Fm with one-hot vector, Ym = Mm = Wm = 0, and αm = 1 M ; S sets J = 0 |