Two dimensional Large Margin Nearest Neighbor for Matrix Classification
Authors: Kun Song, Feiping Nie, Junwei Han
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | At last, promising experimental results on several data sets are provided to show the effectiveness of our method. |
| Researcher Affiliation | Academia | 1School of Automation, Northwestern Polytechnical University, Xi an, 710072, Shaanxi, P. R. China 2School of Computer Science, Northwestern Polytechnical University, Xi an, 710072, P. R. China |
| Pseudocode | Yes | Algorithm 1 2DLMNN |
| Open Source Code | No | The paper does not provide any explicit statement or link for open-sourcing the code for the described methodology. |
| Open Datasets | Yes | We utilize six typical image datasets to evaluate the performance of the proposed method. They are Extended Yale B database [Belhumeur et al., 1997], AR face database1, Coil-1002, USPS handwritten digital database3, UMIST face database4 and POLLEN database5. |
| Dataset Splits | Yes | Each dataset listed in Tabel 1 is randomly divided into training set, validate set and testing set by ratio 2 : 1 : 1. |
| Hardware Specification | Yes | And the algorithms are performed in Matlab on Intel(R) i5-6300HQ @ 2.30 HZ. |
| Software Dependencies | No | The paper mentions that algorithms were performed in Matlab, but it does not specify any version numbers for Matlab or any other software dependencies. |
| Experiment Setup | Yes | In the experiments, we empirically set λ = 0.5 [Parameswaran and Weinberger, 2010; Weinberger and Saul, 2009] and initial points V (0) and U (0) are given by performing 2DPCA [Yang et al., 2004]. We conduct the experiments with different dimensions, and the function value results obtained from 1 to 25 iterations are recorded. The parameter k in KNN is tuned in {1, 2, 3, 4} by cross-validation [Kohavi and others, 1995]. The parameters in 1DLMNN and 2DLMNN are tuned in grid {0.1, , 0.9}. Since our method convergence within 10 iterations, the iteration number of proposed method is set as 10. |