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 [1].
Two-Dimensional PCA with F-Norm Minimization
Authors: Qianqian Wang, Quanxue Gao
AAAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on face image databases illustrate its effectiveness and advantages. |
| Researcher Affiliation | Academia | Qianqian Wang State Key Laboratory of ISN, Xidian University Xi an China Quanxue Gao State Key Laboratory of ISN, Xidian University Xi an China |
| Pseudocode | Yes | Algorithm 1: F -2DPCA |
| Open Source Code | No | The paper does not provide concrete access to source code (specific repository link, explicit code release statement, or code in supplementary materials) for the methodology described in this paper. |
| Open Datasets | Yes | The Extended Yale B database (Georghiades, Belhumeur, and Kriegman 2001) ... In the AR database (Martinez 1998) ... The CMU PIE database (Sim, Baker, and Bsat 2002) |
| Dataset Splits | No | The paper specifies training and testing splits for its datasets (e.g., 'randomly select 32 images... for training, and the remaining images for testing.'), but it does not mention a distinct validation set or split for any of the experiments. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | In our experiments, we use 1-nearest neighbor (1NN) for classification. We set the number of projection vectors as 25 in the Extended Yale B and CMU PIE databases, 30 in the AR database. ... Initialize V(t) Rm k which satisfies VT V = I, t = 1. while not converge do 1. For all training samples, calculate d(t)(i = 1, , N) by Eq. (8). 2. Calculate H(t) according to Eq. (9), i.e., H(t) = N i=1 Ai T di (t)Ai . |