MLGL: An R Package Implementing Correlated Variable Selection by Hierarchical Clustering and Group-Lasso
The R package MLGL, standing for multi-layer group-Lasso, implements a new procedure of variable selection in the context of redundancy between explanatory variables, which holds true with high-dimensional data. A sparsity assumption is made that postulates that only a few variables are relevant fo...
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| Format: | Article |
| Language: | English |
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Foundation for Open Access Statistics
2023-03-01
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| Series: | Journal of Statistical Software |
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| Online Access: | https://www.jstatsoft.org/index.php/jss/article/view/3539 |
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| author | Quentin Grimonprez Samuel Blanck Alain Celisse Guillemette Marot |
| author_facet | Quentin Grimonprez Samuel Blanck Alain Celisse Guillemette Marot |
| author_sort | Quentin Grimonprez |
| collection | DOAJ |
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The R package MLGL, standing for multi-layer group-Lasso, implements a new procedure of variable selection in the context of redundancy between explanatory variables, which holds true with high-dimensional data. A sparsity assumption is made that postulates that only a few variables are relevant for predicting the response variable. In this context, the performance of classical Lasso-based approaches strongly deteriorates as the redundancy increases. The proposed approach combines variables aggregation and selection in order to improve interpretability and performance. First, a hierarchical clustering procedure provides at each level a partition of the variables into groups. Then, the set of groups of variables from the different levels of the hierarchy is given as input to group-Lasso, with weights adapted to the structure of the hierarchy. At this step, group-Lasso outputs sets of candidate groups of variables for each value of the regularization parameter. The versatility offered by package MLGL to choose groups at different levels of the hierarchy a priori induces a high computational complexity. MLGL, however, exploits the structure of the hierarchy and the weights used in group-Lasso to greatly reduce the final time cost. The final choice of the regularization parameter - and therefore the final choice of groups - is made by a multiple hierarchical testing procedure.
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| format | Article |
| id | doaj-art-69f0ec05ef5e4bd9a63b3db029020104 |
| institution | Kabale University |
| issn | 1548-7660 |
| language | English |
| publishDate | 2023-03-01 |
| publisher | Foundation for Open Access Statistics |
| record_format | Article |
| series | Journal of Statistical Software |
| spelling | doaj-art-69f0ec05ef5e4bd9a63b3db0290201042024-12-29T00:12:52ZengFoundation for Open Access StatisticsJournal of Statistical Software1548-76602023-03-01106110.18637/jss.v106.i033386MLGL: An R Package Implementing Correlated Variable Selection by Hierarchical Clustering and Group-LassoQuentin Grimonprez0Samuel Blanck1Alain Celisse2Guillemette Marot3Inria Lille-Nord EuropeUniversité de LilleUniversité Paris 1Université de Lille The R package MLGL, standing for multi-layer group-Lasso, implements a new procedure of variable selection in the context of redundancy between explanatory variables, which holds true with high-dimensional data. A sparsity assumption is made that postulates that only a few variables are relevant for predicting the response variable. In this context, the performance of classical Lasso-based approaches strongly deteriorates as the redundancy increases. The proposed approach combines variables aggregation and selection in order to improve interpretability and performance. First, a hierarchical clustering procedure provides at each level a partition of the variables into groups. Then, the set of groups of variables from the different levels of the hierarchy is given as input to group-Lasso, with weights adapted to the structure of the hierarchy. At this step, group-Lasso outputs sets of candidate groups of variables for each value of the regularization parameter. The versatility offered by package MLGL to choose groups at different levels of the hierarchy a priori induces a high computational complexity. MLGL, however, exploits the structure of the hierarchy and the weights used in group-Lasso to greatly reduce the final time cost. The final choice of the regularization parameter - and therefore the final choice of groups - is made by a multiple hierarchical testing procedure. https://www.jstatsoft.org/index.php/jss/article/view/3539penalized regressioncorrelated variableshierarchical clusteringgroup selectionR |
| spellingShingle | Quentin Grimonprez Samuel Blanck Alain Celisse Guillemette Marot MLGL: An R Package Implementing Correlated Variable Selection by Hierarchical Clustering and Group-Lasso Journal of Statistical Software penalized regression correlated variables hierarchical clustering group selection R |
| title | MLGL: An R Package Implementing Correlated Variable Selection by Hierarchical Clustering and Group-Lasso |
| title_full | MLGL: An R Package Implementing Correlated Variable Selection by Hierarchical Clustering and Group-Lasso |
| title_fullStr | MLGL: An R Package Implementing Correlated Variable Selection by Hierarchical Clustering and Group-Lasso |
| title_full_unstemmed | MLGL: An R Package Implementing Correlated Variable Selection by Hierarchical Clustering and Group-Lasso |
| title_short | MLGL: An R Package Implementing Correlated Variable Selection by Hierarchical Clustering and Group-Lasso |
| title_sort | mlgl an r package implementing correlated variable selection by hierarchical clustering and group lasso |
| topic | penalized regression correlated variables hierarchical clustering group selection R |
| url | https://www.jstatsoft.org/index.php/jss/article/view/3539 |
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