On Semiseparable Kernels and Efficient Computation of Regularized System Identification and Function Estimation. Issue 2 (2020) - Chen, Tianshi; Andersen, Martin S.
Abstract: A long-standing problem for kernel-based regularization methods is their high computational complexity O(N 3 ), where N is the number of data points. In this paper, we show that for semiseparable kernels and some typical input signals, their computational complexity can be lowered to O(Nq 2 ), where q is the output kernel's semiseparability rank that only depends on the chosen kernel and the input signal.