Randomized Kernel Selection With Spectra of Multilevel Circulant Matrices
Authors: Lizhong Ding, Shizhong Liao, Yong Liu, Peng Yang, Xin Gao
AAAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results demonstrate that our randomized kernel selection criteria are significantly more efficient than the existing classic and widely-used criteria while preserving similar predictive performance. |
| Researcher Affiliation | Academia | 1 King Abdullah University of Science and Technology (KAUST), Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, Thuwal, 23955-6900, Saudi Arabia 2 School of Computer Science and Technology, Tianjin University, Tianjin 300350, China 3 Institute of Information Engineering, CAS, Beijing, China |
| Pseudocode | No | The paper describes its methods through mathematical formulations and prose, but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any statement about releasing source code, nor does it provide a link to a code repository. |
| Open Datasets | Yes | A variety of datasets that cover the number of data points ranging from 7200 up to more than 245000 and the number of features ranging from 3 up to 47,236 are selected from the UCI dataset repository3, the LIBSVM dataset repository4, and the KEEL dataset repository5. |
| Dataset Splits | No | We randomly partition each dataset into two parts, with 50% of the data randomly chosen for training and the rest reserved for testing. |
| Hardware Specification | No | It is worth noting that this experiment is conducted on a PC, while the first experiment is conducted on our computing cluster. |
| Software Dependencies | No | We adopt least square support vector machine (LSSVM) as the learning algorithm. |
| Experiment Setup | Yes | The set of Gaussian kernels κ(x x ) = exp( γ x x 2 2) with a variable bandwidth parameter γ {2i, i = 8, 7, . . . , 5, 6} is adopted as the candidate kernel set. The parameter D in the randomized criteria is an important parameter both for the approximation quality and computational efficiency (O(Πm ln(Πm)) = O(l D ln(l D))). We conduct experiments for different values of D (50, 100, 200, 500, 1000, 2000). Finally, we fix D = 100... Since the focus of this work is not on tuning the regularization parameter, it is set as a fixed value 1. |