Omics data usually have ultra-high dimension (p) and small sample size (n). Standard
support vector machines (SVMs), which minimize the L 2 norm for the primal variables, only
lead to sparse solutions for the dual variables. L 1 based SVMs, directly minimizing the L 1
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, L 1 norm is …