Locality Adaptive Discriminant Analysis
Authors: Xuelong Li, Mulin Chen, Feiping Nie, Qi Wang
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Performance on synthetic datasets and real-world benchmark datasets demonstrate the superiority of the proposed method. |
| Researcher Affiliation | Academia | 1School of Computer Science and Center for OPTical IMagery Analysis and Learning (OPTIMAL), Northwestern Polytechnical University, Xi an 710072, Shaanxi, P. R. China 2Unmanned System Research Institute (USRI), Northwestern Polytechnical University, Xi an 710072, Shaanxi, P. R. China |
| Pseudocode | No | The paper describes algorithms and optimization steps in text and equations but does not present them in a structured pseudocode or algorithm block. |
| Open Source Code | No | The paper does not provide any link or explicit statement about the availability of its source code. |
| Open Datasets | Yes | The proposed LADA is evaluated on five standard benchmarks, USPS [Hull, 1994], Mnist [Le Cun et al., 1998], Yale Face [Georghiades et al., 2001], AR Face [Ding and Martinez, 2010], and AMLALL [Golub et al., 1999]. |
| Dataset Splits | No | The paper states 'For each dataset, we randomly choose several samples for training, and use the remaining samples for testing.' but does not provide specific percentages, counts, or predefined splits for reproducibility beyond a general train/test mention. |
| Hardware Specification | No | The paper does not specify any particular hardware (CPU, GPU, etc.) used for running the experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers. |
| Experiment Setup | Yes | For all the datasets, Principal Component Analysis (PCA) [Wold et al., 1987] is performed as the preprocessing step to speed up, and the desired projection direction number is set as 60. |