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..
Orthogonal and Nonnegative Graph Reconstruction for Large Scale Clustering
Authors: Junwei Han, Kai Xiong, Feiping Nie
IJCAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on clustering tasks 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 ONGR Algorithm |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | Yes | A variety of datasets are adopted to evaluate the proposed method. They can be downloaded from the UCI Machine Learning Repository 1, the Lib SVM Data page 2, and three webpages 3 4 5. |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, or detailed splitting methodology) needed to reproduce the data partitioning. It only mentions using standard datasets. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | There are three parameters in ONGR, namely m, s and λ. LSC needs to tune parameters p, r. Nystr om has parameter n1, and KNN-SC has parameter k. The three parameters n2, λ1 and δ are from SSSC. For fair comparison, we took the same kmeans centroids as anchors or landmarks in ONGR-K and LSC-K, and also took the same random selection points in ONGR-R, LSCR, Nystr om and SSSC. For m, p, n1, n2, we searched their value in the range of [100,1200] with step size 100, while searching s and r in the range of [2,8] with step size 1. For KNN-SC, we chose k in the range of [5,20] with step size 5. As suggested by the authors of SSSC, we searched λ1 and δ among {10 7, 10 6, 10 5} and {10 3, 10 2, 10 1}, respectively. For our trade-off parameter λ, we searched log λ in the range of [-6,3] with step size 1. ... Empirically, we set the threshold to be 0.001. |