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 appears if the number of explanatory variables exceeds the number of observations or for some biological reason. Then, the model goodness of fit is penalized by some suitable function of interest. Prominent examples are the lasso, group lasso and sparse-group lasso. Here, we offer a fast and numerically cheap implementation of these operators via proximal gradient descent. The grid search for the penalty parameter is realized by warm starts. The step size between consecutive iterations is determined w

SUBMITTER: Klosa J 

PROVIDER: S-EPMC7493359 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature