Dynamic Hypergraph Structure Learning
Authors: Zizhao Zhang, Haojie Lin, Yue Gao
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on four public datasets show better performance compared with the state-of-the-art methods. |
| Researcher Affiliation | Academia | Zizhao Zhang, Haojie Lin, Yue Gao BNRist, KLISS, School of Software, Tsinghua University, China. zz-zh14@mails.tsinghua.edu.cn, haojie.lin@outlook.com, gaoyue@tsinghua.edu.cn |
| Pseudocode | Yes | Algorithm 1 Dynamic Hypergraph Structure Learning Input: training data set Q, testing data set D, maximal iteration k, and parameters β and λ Output: label projection matrix F and hypergraph structure H 1: Construct the initial hypergraph G = (V, E, W). 2: Initialize the learning rate α. 3: for i = 0 k 1 do 4: Fix H, and update F by Eq. (6). 5: Fix F, and update H by Eq. (8). 6: end for 7: return F, H |
| Open Source Code | No | The paper does not provide an explicit statement or link for open-sourcing the code for the described methodology. |
| Open Datasets | Yes | To evaluate the performance of the proposed method on 3D shape recognition, we have conducted experiments on the National Taiwan University 3D shape dataset (NTU) [Chen et al., 2003] and the Engineering Shape Benchmark (ESB) [Jayanti et al., 2006]. To validate the proposed DHSL method on gesture recognition, we have conducted experiments on the MSR gesture 3D dataset (MSRGesture3D) [Wang et al., 2012] and a hand gesture dataset collected by Huazhong University of Science and Technology (Gesture3DMotion). |
| Dataset Splits | No | The paper describes training and testing data selection processes, but does not explicitly mention a separate validation set or split, nor does it specify how models were selected if no validation set was used. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running experiments, such as GPU or CPU models. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers required to replicate the experiments. |
| Experiment Setup | Yes | In these experiments, we empirically set the parameter β and λ as 10 and 1 on both datasets, respectively. |