Seagull: lasso, group lasso and sparse-group lasso regularization for linear regression models via proximal gradient descent

Seagull: lasso, group lasso and sparse-group lasso regularization for linear regression models via proximal gradient descent

Abstract Background Statistical analyses of biological problems in life sciences often lead to high-dimensional linear models. To solve the corresponding system of equations, penalization approaches are often the methods of choice. They are especially useful in case of multicollinearity, which appea...

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Bibliographic Details
Main Authors: Jan Klosa, Noah Simon, Pål Olof Westermark, Volkmar Liebscher, Dörte Wittenburg
Format: Article
Language:English
Published: BMC 2020-09-01
Series:BMC Bioinformatics
Subjects:
Optimization
Machine learning
High-dimensional data
R package
Online Access:http://link.springer.com/article/10.1186/s12859-020-03725-w

Internet

http://link.springer.com/article/10.1186/s12859-020-03725-w

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