IKA: Independent Kernel Approximator
Machine Learning
2018-09-06 v1 Numerical Analysis
Machine Learning
Abstract
This paper describes a new method for low rank kernel approximation called IKA. The main advantage of IKA is that it produces a function defined as a linear combination of arbitrarily chosen functions. In contrast the approximation produced by Nystr\"om method is a linear combination of kernel evaluations. The proposed method consistently outperformed Nystr\"om method in a comparison on the STL-10 dataset. Numerical results are reproducible using the source code available at https://gitlab.com/matteo-ronchetti/IKA
Keywords
Cite
@article{arxiv.1809.01353,
title = {IKA: Independent Kernel Approximator},
author = {Matteo Ronchetti},
journal= {arXiv preprint arXiv:1809.01353},
year = {2018}
}