Locality Preserving Projection Based on F-norm
Authors: Xiangjie Hu, Yanfeng Sun, Junbin Gao, Yongli Hu, Baocai Yin
AAAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experimental results on three public databases have demonstrated the effectiveness of our proposed methods. |
| Researcher Affiliation | Academia | 1Beijing Advanced Innovation Center for Future Internet Technology 2Faculty of Information Technology, Beijing University of Technology, Beijing, China 3Discipline of Business Analytics. The University of Sydney Business School, University of Sydney, NSW 2006, Australia 4Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, China |
| Pseudocode | Yes | The iterative optimization algorithm for F-LPP is summarized in Algorithm 1. [...] The optimization algorithm of F-2DLPP is summarized in Algorithm 2. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | Yes | The Extended Yale B is face database 1 [...], The CMU-PIE face database 2 [...], The AR face database 3 [...] |
| Dataset Splits | Yes | Extended Yale B: 40 images of each individual are used for the training data and the rest of images are used for testing. CMU-PIE: we use 15 images, a total of 1020 images for training and 6 images, a total of 408 images for testing. AR: we randomly select 13 images of each individual for a total of 1300 images as the training set and the rest for testing. |
| Hardware Specification | No | The paper does not provide specific hardware details used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers. |
| Experiment Setup | No | The paper only mentions that 'In experiments, we use 1 nearest neighbor (1NN) classifier.' without providing concrete hyperparameter values or detailed training configurations for the proposed models. |