Divide-and-Conquer Learning with Nyström: Optimal Rate and Algorithm
Authors: Rong Yin, Yong Liu, Lijing Lu, Weiping Wang, Dan Meng6696-6703
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experimental results on several real-world large-scale datasets containing up to 1M data points show that DC-NY significantly outperforms the state-of-the-art approximate KRLS estimates. |
| Researcher Affiliation | Academia | Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China1 School of Cyber Security, University of Chinese Academy of Sciences, Beijing, China2 |
| Pseudocode | Yes | Algorithm 1 Divide-and-Conquer KRLS with Nystr om (DC-NY) |
| Open Source Code | No | No, the paper provides links to code for baseline methods (RF, NY, DC-RF) but not for their proposed DC-NY algorithm. |
| Open Datasets | Yes | The comparative experiments are based on four real-world datasets: SUSY, HIGGS, Year Prediction MSD and covtype, from website 1. |
| Dataset Splits | Yes | We randomly sample 1 106 data points on SUSY and HIGGS, use the whole of Year Prediction MSD and covtype, and then randomly divide each experimental dataset into training set and prediction set, of which the training set accounts for 70%. |
| Hardware Specification | Yes | Each experiment is measured on a server with 2.40GHz Intel(R) Xeon(R) E52630 v3 CPU and 32 GB of RAM in Matlab. |
| Software Dependencies | No | No, the paper only mentions 'Matlab' without a specific version number and does not list other software dependencies with version details. |
| Experiment Setup | Yes | For ensuring fairness, we use the same way to tune parameters σ in 2[ 2:+0.5:10] and λ in 2[ 21:+1:3], on each dataset and algorithm. |