Options
Iterative L1/2 regularization algorithm for variable selection in the Cox proportional hazards model
Date Issued
2012
ISBN
978-364231019-5
ISSN
16113349
Citation
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 7332 LNCS(PART 2), pp. 11-17
Type
Conference Paper
Abstract
In this paper, we investigate to use theL1/2 regularization method for variable selection based on the Cox's proportional hazards model. The L1/2 regularization method isa reweighed iterative algorithm with the adaptively weighted L1 penalty on regression coefficients. The algorithm of the L1/2 regularization method can be easily obtained by a series of L1 penalties. Simulation results based on standard artificial data show that the L1/2 regularization method can be more accurate for variable selection than Lasso and adaptive Lasso methods. The results from Primary Biliary Cirrhosis (PBC) dataset indicate the L 1/2 regularization method performs competitively. © 2012 Springer-Verlag.
Loading...
Availability at HKSYU Library
