Similarity Learning via Kernel Preserving Embedding
Authors: Zhao Kang, Yiwei Lu, Yuanzhang Su, Changsheng Li, Zenglin Xu4057-4064
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct our experiments with nine benchmark data sets, which are widely used in clustering experiments. We show the statistics of these data sets in Table 1. |
| Researcher Affiliation | Academia | 1School of Computer Science and Engineering, University of Electronic Science and Technology of China, China 2Department of Computer Science, University of Manitoba, 66 Chancellors Cir, Winnipeg, MB R3T 2N2, Canada 3School of Foreign Languages, University of Electronic Science and Technology of China, Sichuan 611731, China |
| Pseudocode | Yes | Algorithm 1: The algorithm of SLKE |
| Open Source Code | Yes | https://github.com/sckangz/SLKE |
| Open Datasets | Yes | We conduct our experiments with nine benchmark data sets, which are widely used in clustering experiments. We show the statistics of these data sets in Table 1. ... YALE, ORL, and JAFEE consist of images of the person. Each image represents different facial expressions or configurations due to times, illumination conditions, and glasses/no glasses. |
| Dataset Splits | No | The paper does not explicitly provide details about training/validation/test dataset splits, specific proportions, or cross-validation methods. It mentions using 'benchmark data sets' for clustering experiments but not how the data was partitioned for model training and validation. |
| Hardware Specification | No | No specific hardware details (such as GPU models, CPU types, or memory specifications) used for running the experiments were mentioned in the paper. |
| Software Dependencies | No | The paper mentions 'PROPACK' and 't-SNE algorithm' but does not provide specific version numbers for these or any other software dependencies used in the experiments. |
| Experiment Setup | Yes | In this subsection, we investigate the influence of our model parameter γ on the clustering results. Take Gaussian kernel with t = 100 of YALE and JAFFE data sets as examples, we plot our algorithm s performance with γ in the range [10 6, 10 5, 10 4, 10 3, 10 2, 10 1] in Figure 2 and 3, respectively. |