Publication at Faculty of Mathematics and Physics |
2023
Abstract
The support vector classification-regression machine for k-class classification (K-SVCR) is a novel multi-class classification approach based on the 1-versus-1-versus-rest structure. In this work, we suggested an efficient model by proposing the p-norm (0<p<1) instead of the 2-norm for the regularization term in the objective function of K-SVCR that can be used for feature selection.
We derived lower bounds for the absolute value of nonzero entries in every local optimal solution of the p-norm based model. Also, we provided upper bounds for the number of nonzero components of the optimal solutions.
We explored the link between solution sparsity, regularization parameters, and the p-choice.