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...
Main Authors: | , , , , |
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Format: | Article |
Language: | English |
Published: |
Hindawi Limited
2013-01-01
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Series: | The Scientific World Journal |
Online Access: | http://dx.doi.org/10.1155/2013/475702 |
Summary: | 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. |
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ISSN: | 1537-744X |