Kernel PCA with the Nyström method
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Kernel methods are powerful but computationally demanding techniques for non-linear learning. A popular remedy, the Nyström method has been shown to be able to scale up kernel methods to very large datasets with little loss in accuracy. However, kernel PCA with the Nyström method has not been widely studied. In this paper we derive kernel PCA with the Nyström method and study its accuracy, providing a finite-sample confidence bound on the difference between the Nyström and standard empirical reconstruction errors. The behaviours of the method and bound are illustrated through extensive computer experiments on real-world data. As an application of the method we present kernel principal component regression with the Nyström method.
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