R package msgl: Multinomial Sparse Group Lasso. Multinomial logistic regression with sparse group lasso penalty. Simultaneous feature selection and parameter estimation for classification. Suitable for high dimensional multiclass classification with many classes. The algorithm computes the sparse group lasso penalized maximum likelihood estimate. Use of parallel computing for cross validation and subsampling is supported through the ’foreach’ and ’doParallel’ packages. Development version is on GitHub, please report package issues on GitHub.
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References in zbMATH (referenced in 7 articles )
Showing results 1 to 7 of 7.
- Agor, Joseph; Özaltın, Osman Y.: Feature selection for classification models via bilevel optimization (2019)
- Li, Ying-Yi; Zhang, Hai-Bin; Li, Fei: A modified proximal gradient method for a family of nonsmooth convex optimization problems (2017)
- Farrell, Max H.: Robust inference on average treatment effects with possibly more covariates than observations (2015)
- Tutz, Gerhard; Pößnecker, Wolfgang; Uhlmann, Lorenz: Variable selection in general multinomial logit models (2015)
- Zhang, Hai-Bin; Jiang, Jiao-Jiao; Zhao, Yun-Bin: On the proximal Landweber Newton method for a class of nonsmooth convex problems (2015)
- Vincent, Martin; Hansen, Niels Richard: Sparse group lasso and high dimensional multinomial classification (2014)
- Zhang, Haibin; Jiang, Jiaojiao; Luo, Zhi-Quan: On the linear convergence of a proximal gradient method for a class of nonsmooth convex minimization problems (2013)