Vertex-Weighted Hypergraph Learning for Multi-View Object Classification
Authors: Lifan Su, Yue Gao, Xibin Zhao, Hai Wan, Ming Gu, Jiaguang Sun
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The proposed method has been evaluated on two public benchmarks, i.e., the NTU and the PSB datasets. Experimental results and comparison with the state-of-the-art methods and recent deep learning method demonstrate the effectiveness of our proposed method. |
| Researcher Affiliation | Academia | Lifan Su, Yue Gao , Xibin Zhao , Hai Wan, Ming Gu, Jiaguang Sun Key Laboratory for Information System Security, Ministry of Education Tsinghua National Laboratory for Information Science and Technology School of Software, Tsinghua University, China. {sulifan,gaoyue,zxb,wanhai,guming,sunjg}@tsinghua.edu.cn |
| Pseudocode | No | The paper describes mathematical formulations and solution procedures but does not include a distinct pseudocode or algorithm block. |
| Open Source Code | No | The paper does not provide any explicit statement or link for open-source code related to the described methodology. |
| Open Datasets | Yes | We have conducted experiments on the National Taiwan University (NTU) 3D model dataset [Chen et al., 2003] and the Princeton Shape Benchmark (PSB) [Shilane et al., 2004]. |
| Dataset Splits | No | The paper specifies training and testing splits but does not explicitly mention a separate validation set. 'In the classification task, 10% to 80% samples for each class are randomly selected as the training data and all left samples are used as the testing data.' |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., CPU, GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers. |
| Experiment Setup | Yes | In all hypergraph-based methods, the number of selected neighbors for hyperedge generation is set as 10, λ is set as 10, and µ is set as 1, respectively. |