Efficient Regularized Regression with L0 Penalty for Variable Selection and Network Construction
Joint Authors
Source
Computational and Mathematical Methods in Medicine
Issue
Vol. 2016, Issue 2016 (31 Dec. 2016), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-10-24
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
Variable selections for regression with high-dimensional big data have found many applications in bioinformatics and computational biology.
One appealing approach is the L0 regularized regression which penalizes the number of nonzero features in the model directly.
However, it is well known that L0 optimization is NP-hard and computationally challenging.
In this paper, we propose efficient EM (L0EM) and dual L0EM (DL0EM) algorithms that directly approximate the L0 optimization problem.
While L0EM is efficient with large sample size, DL0EM is efficient with high-dimensional (n≪m) data.
They also provide a natural solution to all Lp p∈[0,2] problems, including lasso with p=1 and elastic net with p∈[1,2].
The regularized parameter λ can be determined through cross validation or AIC and BIC.
We demonstrate our methods through simulation and high-dimensional genomic data.
The results indicate that L0 has better performance than lasso, SCAD, and MC+, and L0 with AIC or BIC has similar performance as computationally intensive cross validation.
The proposed algorithms are efficient in identifying the nonzero variables with less bias and constructing biologically important networks with high-dimensional big data.
American Psychological Association (APA)
Liu, Zhenqiu& Li, Gang. 2016. Efficient Regularized Regression with L0 Penalty for Variable Selection and Network Construction. Computational and Mathematical Methods in Medicine،Vol. 2016, no. 2016, pp.1-11.
https://search.emarefa.net/detail/BIM-1100111
Modern Language Association (MLA)
Liu, Zhenqiu& Li, Gang. Efficient Regularized Regression with L0 Penalty for Variable Selection and Network Construction. Computational and Mathematical Methods in Medicine No. 2016 (2016), pp.1-11.
https://search.emarefa.net/detail/BIM-1100111
American Medical Association (AMA)
Liu, Zhenqiu& Li, Gang. Efficient Regularized Regression with L0 Penalty for Variable Selection and Network Construction. Computational and Mathematical Methods in Medicine. 2016. Vol. 2016, no. 2016, pp.1-11.
https://search.emarefa.net/detail/BIM-1100111
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1100111