Please use this identifier to cite or link to this item:
http://hdl.handle.net/20.500.11861/7502
Title: | The L1/2 regularization method for variable selection in the Cox model |
Authors: | Liu, Cheng Liang, Yong Luan, Xin-Ze Prof. LEUNG Kwong Sak Chan, Tak-Ming Xu, Zong-Ben Zhang, Hai |
Issue Date: | 2014 |
Source: | Applied Soft Computing, January 2014, Vol. 14, Part C, pp. 498-503 |
Journal: | Applied Soft Computing |
Abstract: | In this paper, we investigate to use the L1/2 regularization method for variable selection based on the Cox's proportional hazards model. The L1/2 regularization can be taken as a representative of Lq (0 < q < 1) regularizations and has been demonstrated many attractive properties. To solve the L1/2 penalized Cox model, we propose a coordinate descent algorithm with a new univariate half thresholding operator which is applicable to high-dimensional biological data. Simulation results based on standard artificial data show that the L1/2 regularization method can be more accurate for variable selection than Lasso and SCAD methods. The results from real DNA microarray datasets indicate the L1/2 regularization method performs competitively. |
Type: | Peer Reviewed Journal Article |
URI: | http://hdl.handle.net/20.500.11861/7502 |
DOI: | 10.1016/j.asoc.2013.09.006 |
Appears in Collections: | Publication |
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