Discriminative Analysis Dictionary Learning
Authors: Jun Guo, Yanqing Guo, Xiangwei Kong, Man Zhang, Ran He
AAAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on several commonly used databases show that our proposed method not only significantly improves the discriminative ability of ADL, but also outperforms state-of-the-art synthesis DL methods. |
| Researcher Affiliation | Academia | 1 School of Information and Communication Engineering, Dalian University of Technology, Dalian 116024, China 2 The Center for Research on Intelligent Perception and Computing, National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China 3 CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Beijing 100190, China |
| Pseudocode | Yes | Algorithm 1 Discriminative Analysis Dictionary Learning |
| Open Source Code | No | The paper does not provide any links or explicit statements about releasing source code for the proposed DADL method. |
| Open Datasets | Yes | We use the features of these databases provided by Jiang2 and Corso3. 2http://www.umiacs.umd.edu/ zhuolin/projectlcksvd.html. 3http://www.cse.buffalo.edu/ jcorso/r/actionbank. |
| Dataset Splits | No | While cross-validation is mentioned for tuning parameters, a distinct "validation dataset split" is not explicitly provided, only training and testing splits are detailed for the final evaluation. |
| Hardware Specification | Yes | Our experiments are run via MATLAB R2013a on a desktop PC with an Intel Core i7-3770 processor at 3.40 GHz and 16.00 GB RAM. |
| Software Dependencies | Yes | Our experiments are run via MATLAB R2013a on a desktop PC with an Intel Core i7-3770 processor at 3.40 GHz and 16.00 GB RAM. |
| Experiment Setup | Yes | We set the Gaussian kernel parameter σ = 10 and the balance weight λ1 = 10 in all our experiments. The experimental results are insensitive to σ [7, 13] and λ1 [10, 15]. The other major parameters (k, λ2, λ3) on each database have been tuned by cross validation. The best (k, λ2, λ3) for each database are listed in Table 1. |