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How do ridge, LASSO and elasticnet regularization methods compare? What are their respective advantages and disadvantages? Any good technical paper, or lecture notes would be appreciated as well.

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up vote 14 down vote accepted

In The Elements of Statistical Learning book, Hastie et al. provide a very insightful and thorough comparison of these shrinkage techniques. The book is available online (pdf). The comparison is done in section 3.4.3, page 69.

The main difference between Lasso and Ridge is the penalty term they use. Ridge uses $L_2$ penalty term which limits the size of the coefficient vector. Lasso uses $L_1$ penalty which imposes sparsity among the coefficients and thus, makes the fitted model more interpretable. Elasticnet is introduced as a compromise between these two techniques, and has a penalty which is a mix of $L_1$ and $L_2$ norms.

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That is a wonderful reference book. – bdeonovic Apr 9 '14 at 15:42
also because the authors are the inventors of these techniques! – Bakaburg Feb 15 at 16:28
Thank you for giving us a reference of this beautiful book – Christina May 18 at 12:32

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