Please use this identifier to cite or link to this item:
PIRA download icon_1.1View/Download Full Text
Title: Sparsity and error analysis of empirical feature-based regularization schemes
Authors: Guo, X 
Fan, J
Zhou, DX
Issue Date: 2016
Source: Journal of machine learning research, 2016, v. 17, no. 89, p. 1-34
Abstract: We consider a learning algorithm generated by a regularization scheme with a concave regularizer for the purpose of achieving sparsity and good learning rates in a least squares regression setting. The regularization is induced for linear combinations of empirical features, constructed in the literatures of kernel principal component analysis and kernel projection machines, based on kernels and samples. In addition to the separability of the involved optimization problem caused by the empirical features, we carry out sparsity and error analysis, giving bounds in the norm of the reproducing kernel Hilbert space, based on a priori conditions which do not require assumptions on sparsity in terms of any basis or system. In particular, we show that as the concave exponent qq of the concave regularizer increases to 11, the learning ability of the algorithm improves. Some numerical simulations for both artificial and real MHC-peptide binding data involving the ℓqℓq regularizer and the SCAD penalty are presented to demonstrate the sparsity and error analysis.
Keywords: Sparsity
Concave regularizer
Reproducing kernel Hilbert space
Regularization with empirical features
SCAD penalty
Publisher: MIT Press
Journal: Journal of machine learning research 
ISSN: 1532-4435
EISSN: 1533-7928
Rights: © 2016 Xin Guo, Jun Fan and Ding-Xuan Zhou.
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (
The following publication Guo, X., Fan, J., & Zhou, D. X. (2016). Sparsity and error analysis of empirical feature-based regularization schemes. The Journal of Machine Learning Research, 17(89), 1-34 is available at
Appears in Collections:Journal/Magazine Article

Files in This Item:
File Description SizeFormat 
Guo_Sparsity_Error_Analysis.pdf430.73 kBAdobe PDFView/Open
Open Access Information
Status open access
File Version Version of Record
View full-text via PolyU eLinks SFX Query
Show full item record

Page views

Last Week
Last month
Citations as of Sep 24, 2023


Citations as of Sep 24, 2023


Last Week
Last month
Citations as of Sep 21, 2023

Google ScholarTM


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.