Kernelization (Schölkopf and Smola, 2002) is a machine learning technique to transform patterns in the data space to a high-dimensional feature space so that the structure of the data can be more easily and adequately discovered.
a nonlinear transformation Φ maps data in R N to H of possibly infinite dimensions (Hilbert space).