Adaptive L1/2 Shooting Regularization Method for Survival Analysis Using Gene Expression Data. (15th December 2013)

• Record Type: Journal Article
• Authors: Liu, Xiao-Ying; Liang, Yong; Xu, Zong-Ben; Zhang, Hai; Leung, Kwong-Sak
• Other Names: Ma J. Academic Editor.; Shen B. Academic Editor.; Wang J. Academic Editor.; Wang J. Academic Editor.
• Is Part Of: TheScientificWorldjournal. Volume 2013(2013)
• Publication Date: 2013-12-15
• Subjects: Science -- Periodicals; Technology -- Periodicals; Medicine -- Periodicals; 505
• DOI: 10.1155/2013/475702
• Languages: English
• ISSNs: 2356-6140

Abstract

Abstract : A new adaptive L 1 / 2 shooting regularization method for variable selection based on the Cox's proportional hazards mode being proposed. This adaptive L 1 / 2 shooting algorithm can be easily obtained by the optimization of a reweighed iterative series of L 1 penalties and a shooting strategy of L 1 / 2 penalty. Simulation results based on high dimensional artificial data show that the adaptive L 1 / 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 L 1 / 2 regularization method performs competitively.