Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Linear Manifold Regularization with Adaptive Graph for Semi-supervised Dimensionality Reduction
Authors: Kai Xiong, Feiping Nie, Junwei Han
IJCAI 2017 | Venue PDF | LLM Run Details | Input Tokens: 18,338 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,575 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| 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. |