Random Gegenbauer Features for Scalable Kernel Methods
Authors: Insu Han, Amir Zandieh, Haim Avron
ICML 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical results show that our proposed features outperform recent kernel approximation methods. and 6. Experiments section. |
| Researcher Affiliation | Academia | 1Yale University 2Max-Planck-Institut f ur Informatik 3Tel Aviv University. |
| Pseudocode | No | The paper does not include a clearly labeled pseudocode or algorithm block. |
| Open Source Code | No | The paper does not provide an explicit statement about releasing source code or a link to a code repository for the described methodology. |
| Open Datasets | Yes | For kernel ridge regression, we use 4 real-world datasets, e.g., Earth Elevation2, CO2 3, Climate4 and Protein5. 2https://github.com/fatiando/rockhound, 3https://db.cger.nies.go.jp/dataset/ODIAC/, 4http://berkeleyearth.lbl.gov/, 5https://archive.ics.uci.edu/. For kernel k-means clustering, we use 6 UCI classification datasets6. 6http://persoal.citius.usc.es/manuel.fernandez.delgado/papers/jmlr/data.tar.gz |
| Dataset Splits | Yes | For all datasets, we randomly split 90% training and 10% testing, and find the ridge parameter via the 2-fold cross-validation on the training set. |
| Hardware Specification | No | The paper does not specify the exact hardware (e.g., CPU, GPU models, or cloud instance types) used for running the experiments. |
| Software Dependencies | No | The paper mentions software such as 'scipy.integrate.quad' and 'scikit-learn' but does not provide specific version numbers for these dependencies. |
| Experiment Setup | Yes | For all kernel approximation methods, we set the final feature dimension to m = 1,024. and The number of clusters is set to the number of classes of each dataset and the number of features are commonly set to m = 512. and find the ridge parameter via the 2-fold cross-validation on the training set. |