Online learning based on iterative projections in sum space of linear and Gaussian reproducing kernel Hilbert spaces

We propose a novel multikernel adaptive filtering algorithm based on the iterative projections in the sum space of reproducing kernel Hilbert spaces. We employ linear and Gaussian kernels, envisioning an application to partially-linear-system identification/estimation. The algorithm is derived by reformulating the hyperplane projection along affine subspace (HYPASS) algorithm in the sum space. The projection is computable by virtue of Minh´s theorem proved in 2010 as long as the input space has nonempty interior. Numerical examples show the efficacy of the proposed algorithm.