Reduced-Set Kernel Principal Components Analysis for Improving the Training and Execution Speed of Kernel Machines

26 Jul 2015Hassan A. KingraviPatricio A. VelaAlexandar Gray

This paper presents a practical, and theoretically well-founded, approach to improve the speed of kernel manifold learning algorithms relying on spectral decomposition. Utilizing recent insights in kernel smoothing and learning with integral operators, we propose Reduced Set KPCA (RSKPCA), which also suggests an easy-to-implement method to remove or replace samples with minimal effect on the empirical operator... (read more)

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