Adaptive L12 Shooting Regularization Method for Survival Analysis Using Gene Expression Data
Joint Authors
Liu, Xiao-Ying
Liang, Yong
Zhang, Hai
Leung, Kwong-Sak
Xu, Zongben
Source
Issue
Vol. 2013, Issue 2013 (31 Dec. 2013), pp.1-5, 5 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2013-12-15
Country of Publication
Egypt
No. of Pages
5
Main Subjects
Medicine
Information Technology and Computer Science
Abstract EN
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.
American Psychological Association (APA)
Liu, Xiao-Ying& Liang, Yong& Xu, Zongben& Zhang, Hai& Leung, Kwong-Sak. 2013. Adaptive L12 Shooting Regularization Method for Survival Analysis Using Gene Expression Data. The Scientific World Journal،Vol. 2013, no. 2013, pp.1-5.
https://search.emarefa.net/detail/BIM-1032961
Modern Language Association (MLA)
Liu, Xiao-Ying…[et al.]. Adaptive L12 Shooting Regularization Method for Survival Analysis Using Gene Expression Data. The Scientific World Journal No. 2013 (2013), pp.1-5.
https://search.emarefa.net/detail/BIM-1032961
American Medical Association (AMA)
Liu, Xiao-Ying& Liang, Yong& Xu, Zongben& Zhang, Hai& Leung, Kwong-Sak. Adaptive L12 Shooting Regularization Method for Survival Analysis Using Gene Expression Data. The Scientific World Journal. 2013. Vol. 2013, no. 2013, pp.1-5.
https://search.emarefa.net/detail/BIM-1032961
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1032961