Random Feature Mapping with Signed Circulant Matrix Projection
Authors: Chang Feng, Qinghua Hu, Shizhong Liao
IJCAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 4 Experiments We implement random feature mappings in R 3.1.1 and conduct experiments on a public SUSE Linux enterprise server 10 SP2 platform with 2.2GHz AMD Opteron Processor 6174 CPU and 48GB RAM. We compare the performance of random feature mappings, including RKS, Fastfood and our SCRF in terms of kernel approximation, efficiency of kernel expansion and generalization performance. |
| Researcher Affiliation | Academia | School of Computer Science and Technology, Tianjin University, Tianjin 300072, China {changfeng,huqinghua,szliao}@tju.edu.cn |
| Pseudocode | No | The paper describes computational steps but does not include any formally structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide an explicit statement about releasing open-source code or a link to a code repository for the methodology described. |
| Open Datasets | Yes | We compare RKS, Fastfood and SCRF with non-linear SVMs on 9 well-known classification benchmark datasets of size ranging from 1,000 to 60,000 with dimensionality ranging from 22 to 3,072. |
| Dataset Splits | Yes | Parameters (C, γ) are selected by 5-fold cross validation with Gaussian kernel SVM. |
| Hardware Specification | Yes | We implement random feature mappings in R 3.1.1 and conduct experiments on a public SUSE Linux enterprise server 10 SP2 platform with 2.2GHz AMD Opteron Processor 6174 CPU and 48GB RAM. |
| Software Dependencies | No | The paper states 'We implement random feature mappings in R 3.1.1' and mentions using 'LIBSVM' and 'LIBLINEAR' but does not specify explicit version numbers for LIBSVM or LIBLINEAR in the text, only citing their papers. |
| Experiment Setup | Yes | Table 2: Comparison of RKS, Fastfood, SCRF with LIBLINEAR and Gaussian kernel with LIBSVM. Parameters (C, γ) are selected by 5-fold cross validation with Gaussian kernel SVM. |