Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/63843
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Applied Mathematics | en_US |
dc.creator | Guo, X | en_US |
dc.creator | Fan, J | en_US |
dc.creator | Zhou, DX | en_US |
dc.date.accessioned | 2017-02-09T08:30:42Z | - |
dc.date.available | 2017-02-09T08:30:42Z | - |
dc.identifier.issn | 1532-4435 | en_US |
dc.identifier.uri | http://hdl.handle.net/10397/63843 | - |
dc.language.iso | en | en_US |
dc.publisher | MIT Press | en_US |
dc.rights | © 2016 Xin Guo, Jun Fan and Ding-Xuan Zhou. | en_US |
dc.rights | This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). | en_US |
dc.rights | 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 https://www.jmlr.org/papers/v17/11-207.html | en_US |
dc.subject | Sparsity | en_US |
dc.subject | Concave regularizer | en_US |
dc.subject | Reproducing kernel Hilbert space | en_US |
dc.subject | Regularization with empirical features | en_US |
dc.subject | lq-penalty | en_US |
dc.subject | SCAD penalty | en_US |
dc.title | Sparsity and error analysis of empirical feature-based regularization schemes | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.spage | 1 | en_US |
dc.identifier.epage | 34 | en_US |
dc.identifier.volume | 17 | en_US |
dc.identifier.issue | 89 | en_US |
dcterms.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. | en_US |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | Journal of machine learning research, 2016, v. 17, no. 89, p. 1-34 | en_US |
dcterms.isPartOf | Journal of machine learning research | en_US |
dcterms.issued | 2016 | - |
dc.identifier.isi | WOS:000391533200001 | - |
dc.identifier.eissn | 1533-7928 | en_US |
dc.identifier.rosgroupid | 2015003399 | - |
dc.description.ros | 2015-2016 > Academic research: refereed > Publication in refereed journal | en_US |
dc.description.oa | Version of Record | en_US |
dc.identifier.FolderNumber | a0965-n02 | - |
dc.identifier.SubFormID | 2245 | - |
dc.description.fundingSource | Self-funded | en_US |
dc.description.pubStatus | Published | en_US |
dc.description.oaCategory | CC | en_US |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Guo_Sparsity_Error_Analysis.pdf | 430.73 kB | Adobe PDF | View/Open |
Page views
148
Last Week
1
1
Last month
Citations as of Sep 8, 2024
Downloads
41
Citations as of Sep 8, 2024
WEB OF SCIENCETM
Citations
24
Last Week
0
0
Last month
Citations as of Jun 20, 2024
Google ScholarTM
Check
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.