Two-Layer Feature Reduction for Sparse-Group Lasso via Decomposition of Convex Sets

NeurIPS 2014 Jie WangJieping Ye

Sparse-Group Lasso (SGL) has been shown to be a powerful regression technique for simultaneously discovering group and within-group sparse patterns by using a combination of the $\ell_1$ and $\ell_2$ norms. However, in large-scale applications, the complexity of the regularizers entails great computational challenges... (read more)

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