  This paper describes a new method for low rank kernel approximation called
IKA. The main advantage of IKA is that it produces a function $\psi(x)$ 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
