Sparse Support Vector Infinite Push

Understand

In this paper, we address the problem of embedded feature selection for ranking on top of the list problems.

• We pose this problem as a regularized empirical risk minimization with $p$-norm push loss function ($p=\infty$) and sparsity inducing regularizers.
• We leverage the issues related to this challenging optimization problem by considering an alternating direction method of multipliers algorithm which is built upon proximal operators of the loss function and the regularizer.
• Our main technical contribution is thus to provide a numerical scheme for computing the infinite push loss function proximal operator.