High-Dimensional Variable Selection With Reciprocal L1-Regularization

@inproceedings{Song2015HighDimensionalVS,
  title={High-Dimensional Variable Selection With Reciprocal L1-Regularization},
  author={Qifan Song and Faming Liang},
  year={2015}
}
During the past decade, penalized likelihood methods have been widely used in variable selection problems, where the penalty functions are typically symmetric about 0, continuous and nondecreasing in (0, ∞). We propose a new penalized likelihood method, reciprocal Lasso (or in short, rLasso), based on a new class of penalty functions that are decreasing in (0, ∞), discontinuous at 0, and converge to infinity when the coefficients approach zero. The new penalty functions give nearly zero… CONTINUE READING

Citations

Publications citing this paper.