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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 ...
For high dimensional supervised learning problems, often using problem specific assumptions can lead to greater accuracy. For problems with grouped covariates, which are believed to have sparse effect...
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 ...