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 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.
Faculty of Information Technology & State Key Laboratory of Quality Research in Chinese Medicines, Macau University of Science and Technology, Macau 999078, China.
Xiao-Ying
XY
Liu
Faculty of Information Technology & State Key Laboratory of Quality Research in Chinese Medicines, Macau University of Science and Technology, Macau 999078, China.
Yong
Y
Liang
Faculty of Science, Xi'an Jiaotong University, Xi'an 710000, China.
Zong-Ben
ZB
Xu
Faculty of Science, Xi'an Jiaotong University, Xi'an 710000, China.
Hai
H
Zhang
Department of Computer Science and Technology, The Chinese University of Hong Kong, Hong Kong 999077, China.
Kwong-Sak
KS
Leung
D000818:Animals; D005786:Gene Expression Regulation; D053208:Kaplan-Meier Estimate; D008954:Models, Biological; D016016:Proportional Hazards Models