Hierarchic Kernel Recursive Least-Squares
Machine Learning
2020-05-01 v2
Abstract
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.
Cite
@article{arxiv.1704.04522,
title = {Hierarchic Kernel Recursive Least-Squares},
author = {Hossein Mohamadipanah and Mahdi Heydari and Girish Chowdhary},
journal= {arXiv preprint arXiv:1704.04522},
year = {2020}
}