IKA: Independent Kernel Approximator
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
PDF AbstractTasks
Datasets
Results from the Paper
Submit
results from this paper
to get state-of-the-art GitHub badges and help the
community compare results to other papers.
Methods
No methods listed for this paper. Add
relevant methods here