Lasso-type recovery of sparse representations for high-dimensional data

The Lasso is an attractive technique for regularization and variable selection for high-dimensional data, where the number of predictor variables p_nis potentially much larger than the number of samples n However, it was recently discovered that the sparsity pattern of the Lasso estimator can only be asymptotically identical to the true sparsity pattern if the design matrix satisfies the so-called irrepresentable condition. The latter...