Abstract
During the last decade, research on Mercer kernel-based learning algorithms has flourished [294, 226, 289]. These algorithms include, for example, the support vector machine (SVM) [63], kernel principal component analysis (KPCA) [289], and kernel Fisher discriminant analysis (KFDA) [219]. The common property of these methods is that they operate linearly, as they are explicitly expressed in terms of inner products in a transformed data space that is a reproducing kernel Hilbert space (RKHS). Most often they correspond to nonlinear operators in the data space, and they are still relatively easy to compute using the so-called “kernel-trick”. The kernel trick is no trick at all; it refers to a property of the RKHS that enables the computation of inner products in a potentially infinite-dimensional feature space, by a simple kernel evaluation in the input space. As we may expect, this is a computational saving step that is one of the big appeals of RKHS. At first glance one may even think that it defeats the “no free lunch theorem” (get something for nothing), but the fact of the matter is that the price of RKHS is the need for regularization and in the memory requirements as they are memory-intensive methods. Kernel-based methods (sometimes also called Mercer kernel methods) have been applied successfully in several applications, such as pattern and object recognition [194], time series prediction [225], and DNA and protein analysis [350], to name just a few.
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Xu, J., Jenssen, R., Paiva, A., Park, I. (2010). A Reproducing Kernel Hilbert Space Framework for ITL. In: Information Theoretic Learning. Information Science and Statistics. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-1570-2_9
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