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L1-regularization path algorithm for generalized linear models
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DOI：10.1111/j.1467-9868.2007.00607.x.png)
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摘要
En 中文
We introduce a path following algorithm for L-1-regularized generalized linear models. The L-1-regularization procedure is useful especially because it, in effect, selects variables according to the amount of penalization on the L-1-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.
Keyword:
generalized linear model
lasso
path algorithm
predictor-corrector method
regularization
variable selection
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期刊
J
IF:
3.6
论文数:
1.5K
被引数:
3.2W
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