On the Equivalence of Linear Discriminant Analysis and Least Squares
Authors: Kibok Lee, Junmo Kim
AAAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results demonstrate the equivalence of the LDA solution and the proposed LS solution. |
| Researcher Affiliation | Collaboration | Kibok Lee1,2 and Junmo Kim1 1Department of Electrical Engineering, KAIST, Daejeon, Korea 2Samsung Electronics DMC R&D Center, Suwon, Korea |
| Pseudocode | Yes | Algorithm 1 Fast approximation of regularized uncorrelated LDA (RULDA) (p = c 1) 1. Compute YB = ZBL, where ZB = {z Bij} in (5). 2. Solve W = argmin W XT CW YT B 2 F + γ W 2 F . 3. return W |
| Open Source Code | No | No. The paper does not provide any explicit statements about releasing source code or links to a code repository for the described methodology. |
| Open Datasets | Yes | We used three data sets for our experiment: the extended Yale Face Database B (Georghiades, Belhumeur, and Kriegman 2001), the MNIST database of handwritten digits (Le Cun and Cortes 1998), and Isolet (Bache and Lichman 2013). |
| Dataset Splits | No | No. The paper provides the number of training and test samples but does not specify a clear split methodology (e.g., percentages, random seed, k-fold cross-validation) or mention a validation set. |
| Hardware Specification | Yes | All experiments were done in MATLAB on a PC with an Intel Core i73610QM CPU at 2.30 GHz and with 8 GB RAM. |
| Software Dependencies | No | No. The paper mentions using 'MATLAB' but does not provide a specific version number for MATLAB or any other software libraries or tools used in the experiments. |
| Experiment Setup | Yes | The regularization parameter is set to be 10 4 on extended Yale B and 1 on the others. |