Learning in High-Dimensional Feature Spaces Using ANOVA-Based Fast Matrix-Vector Multiplication. (arXiv:2111.10140v2 [cs.LG] UPDATED)

Kernel matrices are crucial in many learning tasks such as support vector
machines or kernel ridge regression. The kernel matrix is typically dense and
large-scale. Depending on the dimension of the feature space even the
computation of all of its entries in reasonable time becomes a challenging
task. For such dense matrices the cost of a matrix-vector product scales
quadratically with the dimensionality N , if no customized methods are applied.
We propose the use of an ANOVA kernel, where …

anova arxiv feature learning lg vector