# Hierarchic Kernel Recursive Least-Squares

## TL;DR

This paper introduces a hierarchical kernel recursive least-squares algorithm that models multidimensional data efficiently, achieving faster computation and better accuracy without input space sparsification.

## Contribution

It proposes a novel deep hierarchical structure for kernel models that improves speed and accuracy over traditional single-layer approaches.

## Key findings

- Significant computational speedup demonstrated.
- Improved modeling accuracy shown.
- Effective for evenly distributed multidimensional datasets.

## 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.