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2010

A note on the group lasso and a sparse group lasso

Friedman, J., Hastie, T., Tibshirani, R.

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We consider the group lasso penalty for the linear model.

We note that the standard algorithm for solving the problem assumes that the model matrices in each group are orthonormal.
Here we consider a more general penalty that blends the lasso (L1) with the group lasso ("two-norm").
This penalty yields solutions that are sparse at both the group and individual feature levels.

A note on the group lasso and a sparse group lasso · Around

Built on

[] Bertsekas, D. (1999), Nonlinear programming
1999
Earlier work this paper cites.
[] Yuan, M. & Lin, Y. (2007), ‘Model selection and estimation in regression with grouped variables’, Journal of the Royal Statistical Society, Series B
2007
Earlier work this paper cites.

Similar

[] Meier, L., van de Geer, S. & Bühlmann, P. (2008), ‘The group lasso for logistic regression’, Journal of the Royal Statistical Society B
2008
Cited alongside, same era.

Then

[] Peng, J., Zhu, J., Bergamaschi, A., Han, W., Noh, D.-Y., Pollack, J. R. & Wang, P. (2009), ‘Regularized multivariate regression for identifying master predictors with application to integrative genomics study of breast cancer’, Annals of Applied Statistics (to appear)
2009
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