Sparsity and Error Analysis of Empirical Feature-Based Regularization Schemes
Authors: Xin Guo, Jun Fan, Ding-Xuan Zhou
JMLR 2016 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Some numerical simulations for both artificial and real MHC-peptide binding data involving the ℓq regularizer and the SCAD penalty are presented to demonstrate the sparsity and error analysis. |
| Researcher Affiliation | Academia | Xin Guo EMAIL Department of Applied Mathematics The Hong Kong Polytechnic University Hung Hom, Kowloon, Hong Kong, China Jun Fan EMAIL Department of Statistics University of Wisconsin-Madison 1300 University Avenue, Madison, WI53706, USA Ding-Xuan Zhou EMAIL Department of Mathematics City University of Hong Kong Tat Chee Avenue, Kowloon, Hong Kong, China |
| Pseudocode | No | The paper describes methodologies and mathematical derivations in prose and equations but does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statement about making the source code available, nor does it include any links to code repositories. |
| Open Datasets | Yes | We apply RKPCA to the quantitative Immune Epitope Database (IEDB) benchmark data of human leukocyte antigen (HLA) peptide binding affinities, introduced in (Nielsen et al., 2008). |
| Dataset Splits | Yes | We divide z evenly into five disjoint subsets z = 5 j=1zj, and do 5-fold cross-validation to select the parameter γ from a geometric sequence {10 10, , 10 2} of length 60, to minimize the root-mean-square error (RMSE). |
| Hardware Specification | No | The paper does not provide any specific details regarding the hardware (e.g., CPU, GPU models, memory, or cloud platforms with specifications) used for running the experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies or versions (e.g., programming languages, libraries, or frameworks with version numbers) used for implementing the described methods. |
| Experiment Setup | Yes | We divide z evenly into five disjoint subsets z = 5 j=1zj, and do 5-fold cross-validation to select the parameter γ from a geometric sequence {10 10, , 10 2} of length 60, to minimize the root-mean-mean-square error (RMSE). |