Hypergraph p-Laplacian: A Differential Geometry View
Authors: Shota Saito, Danilo Mandic, Hideyuki Suzuki
AAAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The proposed p-Laplacian is shown to outperform standard hypergraph Laplacians in the experiment on a hypergraph semisupervised learning and normalized cut setting. Our experiment on hypergraph semi-supervised clustering problem shows that our hypergraph p-Laplacian outperforms the current hypergraph Laplacians. |
| Researcher Affiliation | Academia | Shota Saito The University of Tokyo ssaito@sat.t.u-tokyo.ac.jp Danilo P Mandic Imperial College London d.mandic@imperial.ac.uk Hideyuki Suzuki Osaka University hideyuki@ist.osaka.ac.jp |
| Pseudocode | No | The paper describes algorithms and update rules using mathematical equations and textual descriptions, but it does not provide structured pseudocode blocks or algorithm figures. |
| Open Source Code | No | The paper does not provide any statement or link regarding the availability of its source code. |
| Open Datasets | Yes | We summarize the benchmark datasets we used in Table 1. All datasets were taken from UCI Machine Learning Repository. |
| Dataset Splits | Yes | The parameter μ was chosen for all methods from 10k, where k {0, 1, 2, 3, 4} by 5-fold cross validation. We randomly picked up a certain number of labels as known labels, and predicted the remaining ones. We repeated this procedure 10 times for different number of known labels. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run the experiments, such as CPU or GPU models. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers. |
| Experiment Setup | Yes | The parameter μ was chosen for all methods from 10k, where k {0, 1, 2, 3, 4} by 5-fold cross validation. For our p-Laplacian, we varied p from 1 to 3 with the interval of 0.1, and we show the result of p=2 and the result of p giving the smallest average error for each number of known labeled points. The parameter p for Hein s regularizer is fixed at 2, since this is recommended by Hein et al. (2013). We take ψ(0) = y as an initial condition. |