A Survey of L1 Regression
L1 regularization, or regularization with an L1 penalty, is a popular idea in statistics and machine learning. This paper reviews the concept and application of L1 regularization for regression. It is not our aim to present a comprehensive list of the utilities of the L1 penalty in the regression se...
| Main Authors: | , , |
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| Formato: | Journal article |
| Idioma: | English |
| Publicado: |
2013
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| _version_ | 1826276185136431104 |
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| author | Vidaurre, D Bielza, C Larrañaga, P |
| author_facet | Vidaurre, D Bielza, C Larrañaga, P |
| author_sort | Vidaurre, D |
| collection | OXFORD |
| description | L1 regularization, or regularization with an L1 penalty, is a popular idea in statistics and machine learning. This paper reviews the concept and application of L1 regularization for regression. It is not our aim to present a comprehensive list of the utilities of the L1 penalty in the regression setting. Rather, we focus on what we believe is the set of most representative uses of this regularization technique, which we describe in some detail. Thus, we deal with a number of L1-regularized methods for linear regression, generalized linear models, and time series analysis. Although this review targets practice rather than theory, we do give some theoretical details about L1-penalized linear regression, usually referred to as the least absolute shrinkage and selection operator (lasso). © 2013 International Statistical Institute. |
| first_indexed | 2024-03-06T23:10:12Z |
| format | Journal article |
| id | oxford-uuid:6533af59-4f3a-4c31-8039-7a379f9846c2 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-06T23:10:12Z |
| publishDate | 2013 |
| record_format | dspace |
| spelling | oxford-uuid:6533af59-4f3a-4c31-8039-7a379f9846c22022-03-26T18:23:59ZA Survey of L1 RegressionJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:6533af59-4f3a-4c31-8039-7a379f9846c2EnglishSymplectic Elements at Oxford2013Vidaurre, DBielza, CLarrañaga, PL1 regularization, or regularization with an L1 penalty, is a popular idea in statistics and machine learning. This paper reviews the concept and application of L1 regularization for regression. It is not our aim to present a comprehensive list of the utilities of the L1 penalty in the regression setting. Rather, we focus on what we believe is the set of most representative uses of this regularization technique, which we describe in some detail. Thus, we deal with a number of L1-regularized methods for linear regression, generalized linear models, and time series analysis. Although this review targets practice rather than theory, we do give some theoretical details about L1-penalized linear regression, usually referred to as the least absolute shrinkage and selection operator (lasso). © 2013 International Statistical Institute. |
| spellingShingle | Vidaurre, D Bielza, C Larrañaga, P A Survey of L1 Regression |
| title | A Survey of L1 Regression |
| title_full | A Survey of L1 Regression |
| title_fullStr | A Survey of L1 Regression |
| title_full_unstemmed | A Survey of L1 Regression |
| title_short | A Survey of L1 Regression |
| title_sort | survey of l1 regression |
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