IKA: Independent Kernel Approximator

09/05/2018

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by   Matteo Ronchetti, et al.

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This paper describes a new method for low rank kernel approximation called IKA. The main advantage of IKA is that it produces a function ψ(x) defined as a linear combination of arbitrarily chosen functions. In contrast the approximation produced by Nyström method is a linear combination of kernel evaluations. The proposed method consistently outperformed Nyström 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