Sparse Support Vector Infinite Push
This addresses feature selection in ranking tasks for applications like bioinformatics and brain-computer interfaces, but is incremental as it builds on existing optimization frameworks.
The paper tackles embedded feature selection for ranking on top of the list problems by formulating it as a regularized empirical risk minimization with an infinite push loss function and sparsity-inducing regularizers, and shows that their algorithm achieves competitive ranking performance while using fewer variables than competitors.
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. Experimental results on toy, DNA microarray and BCI problems show how our novel algorithm compares favorably to competitors for ranking on top while using fewer variables in the scoring function.