Elastic net regularization

The Elastic Net is a regularized regression method which overcome the limitations of The LASSO (least absolute shrinkage and selection operator) method with the constraint that $\|\beta \|_{1}$. For example, in the "large p, small n problem" case, the LASSO selects at most n variables before it saturates. Also if there is a group of highly correlated variables, then the LASSO tends to select one variable from a group and ignore the others.

References

• Zou, Hui (2005). "Regularization and Variable Selection via the Elastic Net". Journal of the Royal Statistical Society B. MA, USA: Blackwell Publishing, Inc.: 301–320. {{cite journal}}: Unknown parameter |coauthors= ignored (|author= suggested) (help)

External links

• http://www.stanford.edu/~hastie/TALKS/enet_talk.pdf