Linear Manifold Regularization with Adaptive Graph for Semi-supervised Dimensionality Reduction
Authors: Kai Xiong, Feiping Nie, Junwei Han
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on several benchmark datasets demonstrate the effectiveness of the proposed method. |
| Researcher Affiliation | Academia | 1Northwestern Ploytechnical University, Xi an, 710072, P. R. China 2University of Texas at Arlington, USA |
| Pseudocode | Yes | Algorithm 1 The Proposed Method LMRAG |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | Yes | We use several widely used benchmark datasets JAFFE1, CMU PIE [Sim et al., 2003], UMIST2, YALE, YALE-B3, Corel [Chen et al., 2011] and COIL-204 to evaluate the proposed LMRAG in our experiments. 1http://www.kasrl.org/jaffe.html, 3http://www.cad.zju.edu.cn/home/dengcai/Data/data.html, 4http://www.cs.columbia.edu/CAVE/software/softlib/coil20.php |
| Dataset Splits | Yes | We randomly chose 40% samples per class as the training data, and used the remaining 60% as the test data. Among the training data, we randomly selected p = {1, 2, 3} samples per class as the labeled data, and used the remaining as the unlabeled data. |
| Hardware Specification | No | The paper does not explicitly describe the hardware used for experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers. |
| Experiment Setup | Yes | The parameters α and β in LMRAG, SDA, TR-FSDA and SSDL5, µ and γ in FME, γA and γI in Lap RLS/L need to be tuned, respectively. We searched their values in the range of {10 6, 10 4, 10 2, 100, 102, 104, 106}. For fair comparison, the reduced dimensionality was fixed as c in SDA, TR-FSDA and SSDL. We uniformly set the neighbor number k to 5 and chose the band width σ of Gaussian kernel in a self-tuning way [Chen et al., 2011] while evaluating the classification performance. |