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Article: The flare package for high dimensional linear regression and precision matrix estimation in R
Title | The flare package for high dimensional linear regression and precision matrix estimation in R |
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Authors | |
Keywords | Sparse linear regression Sparse precision matrix estimation Tuning insensitiveness Robustness Alternating direction method of multipliers |
Issue Date | 2015 |
Citation | Journal of Machine Learning Research, 2015, v. 16, p. 553-557 How to Cite? |
Abstract | ©2015 Xingguo Li, Tuo Zhao, Xiaoming Yuan and Han Liu. This paper describes an R package named flare, which implements a family of new high dimensional regression methods (LAD Lasso, SQRT Lasso, â < inf > q < /inf > Lasso, and Dantzig selector) and their extensions to sparse precision matrix estimation (TIGER and CLIME). These methods exploit different nonsmooth loss functions to gain modeling flexibility, estimation robustness, and tuning insensitiveness. The developed solver is based on the alternating direction method of multipliers (ADMM). The package flare is coded in double precision C, and called from R by a user-friendly interface. The memory usage is optimized by using the sparse matrix output. The experiments show that flare is efficient and can scale up to large problems. |
Persistent Identifier | http://hdl.handle.net/10722/251107 |
ISSN | 2023 Impact Factor: 4.3 2023 SCImago Journal Rankings: 2.796 |
DC Field | Value | Language |
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dc.contributor.author | Li, Xingguo | - |
dc.contributor.author | Zhao, Tuo | - |
dc.contributor.author | Yuan, Xiaoming | - |
dc.contributor.author | Liu, Han | - |
dc.date.accessioned | 2018-02-01T01:54:35Z | - |
dc.date.available | 2018-02-01T01:54:35Z | - |
dc.date.issued | 2015 | - |
dc.identifier.citation | Journal of Machine Learning Research, 2015, v. 16, p. 553-557 | - |
dc.identifier.issn | 1532-4435 | - |
dc.identifier.uri | http://hdl.handle.net/10722/251107 | - |
dc.description.abstract | ©2015 Xingguo Li, Tuo Zhao, Xiaoming Yuan and Han Liu. This paper describes an R package named flare, which implements a family of new high dimensional regression methods (LAD Lasso, SQRT Lasso, â < inf > q < /inf > Lasso, and Dantzig selector) and their extensions to sparse precision matrix estimation (TIGER and CLIME). These methods exploit different nonsmooth loss functions to gain modeling flexibility, estimation robustness, and tuning insensitiveness. The developed solver is based on the alternating direction method of multipliers (ADMM). The package flare is coded in double precision C, and called from R by a user-friendly interface. The memory usage is optimized by using the sparse matrix output. The experiments show that flare is efficient and can scale up to large problems. | - |
dc.language | eng | - |
dc.relation.ispartof | Journal of Machine Learning Research | - |
dc.subject | Sparse linear regression | - |
dc.subject | Sparse precision matrix estimation | - |
dc.subject | Tuning insensitiveness | - |
dc.subject | Robustness | - |
dc.subject | Alternating direction method of multipliers | - |
dc.title | The flare package for high dimensional linear regression and precision matrix estimation in R | - |
dc.type | Article | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.scopus | eid_2-s2.0-84930944534 | - |
dc.identifier.volume | 16 | - |
dc.identifier.spage | 553 | - |
dc.identifier.epage | 557 | - |
dc.identifier.eissn | 1533-7928 | - |
dc.identifier.issnl | 1532-4435 | - |