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- Publisher Website: 10.1016/j.csda.2014.05.017
- Scopus: eid_2-s2.0-84903161800
- WOS: WOS:000340139900015
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Article: Linearized alternating direction method of multipliers for sparse group and fused LASSO models
Title | Linearized alternating direction method of multipliers for sparse group and fused LASSO models |
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Authors | |
Keywords | Variable selection Least absolute shrinkage and selection operator Alternating direction method of multipliers Convex optimization Linear regression |
Issue Date | 2014 |
Citation | Computational Statistics and Data Analysis, 2014, v. 79, p. 203-221 How to Cite? |
Abstract | 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. © 2014 Elsevier B.V. All rights reserved. |
Persistent Identifier | http://hdl.handle.net/10722/251073 |
ISSN | 2023 Impact Factor: 1.5 2023 SCImago Journal Rankings: 1.008 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Li, Xinxin | - |
dc.contributor.author | Mo, Lili | - |
dc.contributor.author | Yuan, Xiaoming | - |
dc.contributor.author | Zhang, Jianzhong | - |
dc.date.accessioned | 2018-02-01T01:54:30Z | - |
dc.date.available | 2018-02-01T01:54:30Z | - |
dc.date.issued | 2014 | - |
dc.identifier.citation | Computational Statistics and Data Analysis, 2014, v. 79, p. 203-221 | - |
dc.identifier.issn | 0167-9473 | - |
dc.identifier.uri | http://hdl.handle.net/10722/251073 | - |
dc.description.abstract | 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. © 2014 Elsevier B.V. All rights reserved. | - |
dc.language | eng | - |
dc.relation.ispartof | Computational Statistics and Data Analysis | - |
dc.subject | Variable selection | - |
dc.subject | Least absolute shrinkage and selection operator | - |
dc.subject | Alternating direction method of multipliers | - |
dc.subject | Convex optimization | - |
dc.subject | Linear regression | - |
dc.title | Linearized alternating direction method of multipliers for sparse group and fused LASSO models | - |
dc.type | Article | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1016/j.csda.2014.05.017 | - |
dc.identifier.scopus | eid_2-s2.0-84903161800 | - |
dc.identifier.volume | 79 | - |
dc.identifier.spage | 203 | - |
dc.identifier.epage | 221 | - |
dc.identifier.isi | WOS:000340139900015 | - |
dc.identifier.issnl | 0167-9473 | - |