Sparse support vector machines with L₀ approximation for ultra-high dimensional omics data.

ABSTRACT: Omics data usually have ultra-high dimension (p) and small sample size (n). Standard support vector machines (SVMs), which minimize the L₂ norm for the primal variables, only lead to sparse solutions for the dual variables. L₁ based SVMs, directly minimizing the L₁ norm, have been used for feature selection with omics data. However, most current methods directly solve the primal formulations of the problem, which are not computationally scalable. The computational complexity increases with the number of features. In addition, L₁ norm is known to be asymptotically biased and not consistent for feature selection. In this paper, we develop an efficient method for sparse support vector machines with L₀ norm approximation. The proposed meth

SUBMITTER: Liu Z 

PROVIDER: S-EPMC6553498 | biostudies-literature | 2019 May

REPOSITORIES: biostudies-literature