Adaptive L1/2 Shooting Regularization Method for Survival Analysis Using Gene Expression Data

A new adaptive L1/2 shooting regularization method for variable selection based on the Cox’s proportional hazards mode being proposed. This adaptive L1/2 shooting algorithm can be easily obtained by the optimization of a reweighed iterative series of L1 penalties and a shooting strategy of L1/2 pena...

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Main Authors: Xiao-Ying Liu, Yong Liang, Zong-Ben Xu, Hai Zhang, Kwong-Sak Leung
Format: Article
Language:English
Published: Wiley 2013-01-01
Series:The Scientific World Journal
Online Access:http://dx.doi.org/10.1155/2013/475702
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author Xiao-Ying Liu
Yong Liang
Zong-Ben Xu
Hai Zhang
Kwong-Sak Leung
author_facet Xiao-Ying Liu
Yong Liang
Zong-Ben Xu
Hai Zhang
Kwong-Sak Leung
author_sort Xiao-Ying Liu
collection DOAJ
description A new adaptive L1/2 shooting regularization method for variable selection based on the Cox’s proportional hazards mode being proposed. This adaptive L1/2 shooting algorithm can be easily obtained by the optimization of a reweighed iterative series of L1 penalties and a shooting strategy of L1/2 penalty. Simulation results based on high dimensional artificial data show that the adaptive L1/2 shooting regularization method can be more accurate for variable selection than Lasso and adaptive Lasso methods. The results from real gene expression dataset (DLBCL) also indicate that the L1/2 regularization method performs competitively.
format Article
id doaj-art-b021f4d3a29d4e0cbd579615e7340069
institution DOAJ
issn 1537-744X
language English
publishDate 2013-01-01
publisher Wiley
record_format Article
series The Scientific World Journal
spelling doaj-art-b021f4d3a29d4e0cbd579615e73400692025-08-20T03:04:30ZengWileyThe Scientific World Journal1537-744X2013-01-01201310.1155/2013/475702475702Adaptive L1/2 Shooting Regularization Method for Survival Analysis Using Gene Expression DataXiao-Ying Liu0Yong Liang1Zong-Ben Xu2Hai Zhang3Kwong-Sak Leung4Faculty of Information Technology & State Key Laboratory of Quality Research in Chinese Medicines, Macau University of Science and Technology, Macau 999078, ChinaFaculty of Information Technology & State Key Laboratory of Quality Research in Chinese Medicines, Macau University of Science and Technology, Macau 999078, ChinaFaculty of Science, Xi’an Jiaotong University, Xi’an 710000, ChinaFaculty of Science, Xi’an Jiaotong University, Xi’an 710000, ChinaDepartment of Computer Science and Technology, The Chinese University of Hong Kong, Hong Kong 999077, ChinaA new adaptive L1/2 shooting regularization method for variable selection based on the Cox’s proportional hazards mode being proposed. This adaptive L1/2 shooting algorithm can be easily obtained by the optimization of a reweighed iterative series of L1 penalties and a shooting strategy of L1/2 penalty. Simulation results based on high dimensional artificial data show that the adaptive L1/2 shooting regularization method can be more accurate for variable selection than Lasso and adaptive Lasso methods. The results from real gene expression dataset (DLBCL) also indicate that the L1/2 regularization method performs competitively.http://dx.doi.org/10.1155/2013/475702
spellingShingle Xiao-Ying Liu
Yong Liang
Zong-Ben Xu
Hai Zhang
Kwong-Sak Leung
Adaptive L1/2 Shooting Regularization Method for Survival Analysis Using Gene Expression Data
The Scientific World Journal
title Adaptive L1/2 Shooting Regularization Method for Survival Analysis Using Gene Expression Data
title_full Adaptive L1/2 Shooting Regularization Method for Survival Analysis Using Gene Expression Data
title_fullStr Adaptive L1/2 Shooting Regularization Method for Survival Analysis Using Gene Expression Data
title_full_unstemmed Adaptive L1/2 Shooting Regularization Method for Survival Analysis Using Gene Expression Data
title_short Adaptive L1/2 Shooting Regularization Method for Survival Analysis Using Gene Expression Data
title_sort adaptive l1 2 shooting regularization method for survival analysis using gene expression data
url http://dx.doi.org/10.1155/2013/475702
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AT yongliang adaptivel12shootingregularizationmethodforsurvivalanalysisusinggeneexpressiondata
AT zongbenxu adaptivel12shootingregularizationmethodforsurvivalanalysisusinggeneexpressiondata
AT haizhang adaptivel12shootingregularizationmethodforsurvivalanalysisusinggeneexpressiondata
AT kwongsakleung adaptivel12shootingregularizationmethodforsurvivalanalysisusinggeneexpressiondata