Hierarchic Kernel Recursive Least-Squares
This work addresses computational efficiency and accuracy challenges in kernel-based modeling for multidimensional datasets, representing an incremental improvement over existing methods.
The authors tackled the problem of modeling evenly distributed multidimensional datasets with kernel-based methods by introducing a deep hierarchical structure that models kernel weights across dimensions, resulting in significant computational speedup and improved modeling accuracy.
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.