Journal
COMPUTATIONAL STATISTICS & DATA ANALYSIS
Volume 79, Issue -, Pages 203-221Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.csda.2014.05.017
Keywords
Least absolute shrinkage and selection operator; Alternating direction method of multipliers; Variable selection; Linear regression; Convex optimization
Funding
- Research Grants Council, University Grants Committee, Hong Kong [203712]
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The least absolute shrinkage and selection operator (LASSO) has been playing an important role in variable selection and dimensionality reduction for linear regression. In this paper we focus on two general LASSO models: Sparse Group LASSO and Fused LASSO, and apply the linearized alternating direction method of multipliers (LADMM for short) to solve them. The LADMM approach is shown to be a very simple and efficient approach to numerically solve these general LASSO models. We compare it with some benchmark approaches on both synthetic and real datasets. (C) 2014 Elsevier B.V. All rights reserved.
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