Notice: 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. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Semi-supervised Orthogonal Graph Embedding with Recursive Projections
Authors: Hanyang Liu, Junwei Han, Feiping Nie
IJCAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Comprehensive experiment on several benchmarks demonstrates the significant improvement over the existing methods. |
| Researcher Affiliation | Academia | Hanyang Liu1, Junwei Han1 , Feiping Nie1,2 1 Northwestern Polytechnical University, Xi an 710072, P. R. China 2 University of Texas at Arlington, USA |
| Pseudocode | Yes | Algorithm 1 Algorithm to solve problem in Eq.(16) |
| Open Source Code | No | The paper does not provide concrete access to source code for the described methodology. |
| Open Datasets | Yes | In our experiments, we use six real world benchmarks including three face benchmarks (JAFFE1, AT&T2, and CMU-PIE), a handwritten digits dataset MNIST, and two object benchmarks (COIL-20 and MPEG73). 1http://www.kasrl.org/jaffe.html 2http://www.cl.cam.ac.uk/research/dtg/attarchive.html 3http://www.dabi.temple.edu/ shape/MPEG7/dataset.html |
| Dataset Splits | No | The paper describes training and testing splits and labeled/unlabeled data, but does not explicitly mention a separate validation dataset split. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU, GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers. |
| Experiment Setup | Yes | In SOGE, we set the weight µ in the diagonal matrix U as 100 for all datasets. In order to fairly compare SOGE with other algorithms, we tuned all the regularization parameters involved in each algorithms with grid search within {10 9, 10 6, 10 3, 100, 103, 106, 109}. For all the algorithms, we employ the k-nearest neighbor (k NN) classifier to evaluate the performance of dimensionality reduction, and set k = 1 in k NN for all the algorithms. For all the datasets, we use PCA as a preprocessing procedure to denoise all the data with 95% of the information preserved, similarly as in [Yan et al., 2007]. |