Multikernel least squares estimation

The multikernel least squares (MKLS) algorithm for multivariate nonlinear estimation of vector-valued signals is introduced. This is achieved by finding optimal combinations of subkernels, in the least squares sense, which are specific for different regions of the input space. Sufficient conditions for the existence of Wiener solutions for both the monokernel and multikernel approaches are provided, and uniqueness of the multikernel structure is illuminated. The ability of the proposed MKLS to replicate non-homogeneous nonlinear multivariate mappings is illustrated both analytically and by comparison with its monokernel counterpart for the prediction of synthetic benchmark data and real-world body sensor multivariate data.