AFFINELY CONSTRAINED ONLINE LEARNING AND ITS APPLICATION TO BEAMFORMING

This paper presents a novel method for incorporating a-priori affine constraints in online kernel-based learning tasks. The proposed technique elaborates the generic tool of projections to form a sequence of estimates in Reproducing Kernel Hilbert Spaces (RKHS). The method guarantees that the whole sequence of estimates lies in the given affine constraint set. To validate the algorithm, a beamforming, task is considered. The numerical results show that the proposed frame provides with solutions in cases where the classical linear approach collapses, and forms proper beam-patterns as opposed to a recent unconstrained kernel-based regression method