Quantized Fourier and Polynomial Features for more Expressive Tensor Network Models

arXiv:2309.05436v2 Announce Type: replace
Abstract: In the context of kernel machines, polynomial and Fourier features are commonly used to provide a nonlinear extension to linear models by mapping the data to a higher-dimensional space. Unless one considers the dual formulation of the learning problem, which renders exact large-scale learning unfeasible, the exponential increase of model parameters in the dimensionality of the data caused by their tensor-product structure prohibits to tackle high-dimensional problems. One of the possible approaches to circumvent this …

abstract arxiv context cs.lg data extension features fourier kernel linear machines mapping network polynomial scale space tensor type