IKA: Independent Kernel Approximator

arXiv1809.01353MaRDI QIDQ6306267

Author name not available (Why is that?)

Publication date: 5 September 2018

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 $psi(x)$ 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

Has companion code repository: https://gitlab.com/matteo-ronchetti/IKA

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