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..
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 . |