gglasso: Group Lasso Penalized Learning Using A Unified BMD Algorithm. This package implements a unified algorithm, blockwise-majorization-decent (BMD), for efficiently computing the solution paths of the group-lasso penalized least squares, logistic regression, Huberized SVM and squared SVM.
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References in zbMATH (referenced in 4 articles )
Showing results 1 to 4 of 4.
- Fan, Zengyan; Lian, Heng: Quantile regression for additive coefficient models in high dimensions (2018)
- Yang, Lei; Lv, Shaogao; Wang, Junhui: Model-free variable selection in reproducing kernel Hilbert space (2016)
- Mai, Qing; Zou, Hui: The fused Kolmogorov filter: a nonparametric model-free screening method (2015)
- Yang, Yi; Zou, Hui: A fast unified algorithm for solving group-lasso penalize learning problems (2015)