Department of Mathematics
An R Package flare for High Dimensional Linear Regression and Precision Matrix Estimation
This paper describes an R package named flare, which implements a family of new high dimensional regression methods (LAD Lasso, SQRT Lasso, ℓqℓq Lasso, and Dantzig selector) and their extensions to sparse precision matrix estimation (TIGER and CLIME). These methods exploit different nonsmooth loss functions to gain modeling flexibility, estimation robustness, and tuning insensitiveness. The developed solver is based on the alternating direction method of multipliers (ADMM). The package flare is coded in double precision C, and called from R by a user-friendly interface. The memory usage is optimized by using the sparse matrix output. The experiments show that flare is efficient and can scale up to large problems.
sparse linear regression, sparse precision matrix estimation, alternating direction method of multipliers, robustness, tuning insensitiveness
Source Publication Title
Journal of Machine Learning Research
Link to Publisher's Edition
Li, X., Zhao, T., Yuan, X., & Liu, H. (2015). An R Package flare for High Dimensional Linear Regression and Precision Matrix Estimation. Journal of Machine Learning Research, 16, 553-557. Retrieved from https://repository.hkbu.edu.hk/hkbu_staff_publication/5887