Uncorrelated Multi-View Discrimination Dictionary Learning for Recognition
Authors: Xiao-Yuan Jing, Rui-Min Hu, Fei Wu, Xi-Lin Chen, Qian Liu, Yong-Fang Yao
AAAI 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on several public datasets demonstrate the effectiveness of the proposed approach. |
| Researcher Affiliation | Academia | 1State Key Laboratory of Software Engineering, School of Computer, Wuhan University, China 2National Engineering Research Center for Multimedia Software, School of Computer, Wuhan University, China 3College of Automation, Nanjing University of Posts and Telecommunications, China 4Institute of Computing Technology, Chinese Academy of Sciences, China |
| Pseudocode | Yes | Algorithm 1. UMD2L |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | Yes | The proposed UMD2L approach is verified on the Multi PIE (Cai et al., 2006), AR (Martinez and Benavente 1998), COIL-20 (Murase and Nayar 1995) and MNIST (Le Cun et al., 1998) datasets. |
| Dataset Splits | Yes | In all experiments, the tuning parameters in UMD2L ( O in dictionary learning phase, and J in classification phase) and the parameters of all compared methods are evaluated by 5-fold cross validation to avoid over-fitting. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper mentions techniques like PCA transformation, Gabor transformation, Karhunen-Loeve (KL) transformation, and Local Binary Patterns (LBP), but does not specify software names with version numbers. |
| Experiment Setup | Yes | Concretely, the parameters of UMD2L are set as 0.005 O and 0.001 J for three datasets. In addition, the default dictionary atoms number for each view in UMD2L is set as the number of training samples. |