Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Discriminative Analysis Dictionary Learning
Authors: Jun Guo, Yanqing Guo, Xiangwei Kong, Man Zhang, Ran He
AAAI 2016 | Venue PDF | 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. |