Efficient Regularized Regression with L₀ Penalty for Variable Selection and Network Construction.

ABSTRACT: Variable selections for regression with high-dimensional big data have found many applications in bioinformatics and computational biology. One appealing approach is the L₀ regularized regression which penalizes the number of nonzero features in the model directly. However, it is well known that L₀ optimization is NP-hard and computationally challenging. In this paper, we propose efficient EM (L₀EM) and dual L₀EM (DL₀EM) algorithms that directly approximate the L₀ optimization problem. While L₀EM is efficient with large sample size, DL₀EM is efficient with high-dimensional (n ≪ m) data. They also provide a natural solution to all L

SUBMITTER: Liu Z 

PROVIDER: S-EPMC5098106 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature