Optimizing the regularization parameters selection in sparse modeling

VICTORIA PETERSON; SPIES, RUBEN DANIEL

Workshop; Neural Information Processing Systems Conference: LatinX in AI (LXAI) Research Workshop 2019,; 2019

Tikhonov functionals are commonly used as regularization strategies for severely ill-posed inverse problems. Besides the type of penalization induced into the solution, the proper selection of the regularization parameters is of utmost importance for accurate solutions. In this work, we analyze several data-driven regularization parameters estimation methods in a mixed-term discriminative framework. Numerical results for P300 detection in Brain-Computer Interfaces classification are presented, showing the impact of regularization parameter estimation into classification performance.end{abstract}