Deep kernel recursive least-squares algorithm - Mohamadipanah, Hossein; Heydari, Mahdi; Chowdhary, Girish (Nonlinear dynamics, 2021)
We present a new kernel-based algorithm for modeling evenly distributed multidimensional datasets that does not rely on input space sparsification. The presented method reorganizes the typical single-layer kernel-based model into a deep hierarchical structure, such that the weights of a kernel model over each dimension are modeled over its adjacent dimension. We show that modeling weights in the suggested structure leads to significant computational speedup and improved modeling accuracy.