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.
Signed Laplacian Embedding for Supervised Dimension Reduction
Authors: Chen Gong, Dacheng Tao, Jie Yang, Keren Fu
AAAI 2014 | Venue PDF | LLM Run Details | Input Tokens: 14,175 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,040 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 | Thorough empirical studies on synthetic and real datasets demonstrate the effectiveness of SLE. |
| Researcher Affiliation | Collaboration | Chen Gong ,? and Dacheng Tao? and Jie Yang and Keren Fu Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University ?Centre for Quantum Computation and Intelligent Systems, University of Technology Sydney EMAIL EMAIL |
| Pseudocode | No | The paper describes the algorithm steps in text but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | Yes | Six UCI datasets (Frank and Asuncion 2010), Breast Cancer, Musk, Waveform, Seeds, SPECT and Wine, were adopted to test the dimension reduction performances of the algorithms compared. Yale (Georghiades, Belhumeur, and Kriegman 2001) and Labeled Face in the Wild (LFW) (Gary et al. 2007). |
| Dataset Splits | No | The paper does not explicitly provide validation dataset splits. It mentions random splits into training and test sets but not a separate validation split or explicit cross-validation methodology. |
| Hardware Specification | No | The paper does not provide specific hardware details used for running its experiments. |
| Software Dependencies | No | The paper mentions software components and algorithms (e.g., RBF kernel, NNC classifier, QZ algorithm, Krylov subspace algorithm) but does not provide specific version numbers for any of them. |
| Experiment Setup | Yes | The RBF kernel width σ in LPP, MFA and DLA was chosen from the set {0.01, 0.1, 0.5, 1, 10}. The numbers of neighbors for graph construction, such as k in LPP, k1 and k2 in MFA and DLA, were chosen from {5, 10, 15, 20, 25, 30}. We built a 5NN graph for LPP with kernel width σ = 10. In MFA and DLA, we set k1 = 5 and k2 = 10 to obtain the best performances. k and σ were adjusted to 10 and 1 for LPP, and we set k1 = k2 = 10 for MFA and DLA. |