Skip to main content

L1-regularization path algorithm for generalized linear models

Buy Article:

$51.00 plus tax (Refund Policy)



We introduce a path following algorithm for L1-regularized generalized linear models. The L1-regularization procedure is useful especially because it, in effect, selects variables according to the amount of penalization on the L1-norm of the coefficients, in a manner that is less greedy than forward selection–backward deletion. The generalized linear model path algorithm efficiently computes solutions along the entire regularization path by using the predictor–corrector method of convex optimization. Selecting the step length of the regularization parameter is critical in controlling the overall accuracy of the paths; we suggest intuitive and flexible strategies for choosing appropriate values. We demonstrate the implementation with several simulated and real data sets.

Keywords: Generalized linear model; Lasso; Path algorithm; Predictor–corrector method; Regularization; Variable selection

Document Type: Research Article


Affiliations: 1: Google Inc., Mountain View, USA 2: Stanford University, USA

Publication date: September 1, 2007


Access Key

Free Content
Free content
New Content
New content
Open Access Content
Open access content
Subscribed Content
Subscribed content
Free Trial Content
Free trial content
Cookie Policy
Cookie Policy
ingentaconnect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more