Orthogonal and Nonnegative Graph Reconstruction for Large Scale Clustering
Authors: Junwei Han, Kai Xiong, Feiping Nie
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | 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. |