Data Science, Kernel Trick, Nonlinear Models, Machine Learning, Data Transformation.
to explicitly mapping the feature-space 𝑋X to kernel-space ΦΦ. This is an advantage because for some mapping functions 𝜙(𝑥)ϕ(x) the resulting dimension in kernel-space can become infinitely large and, therefore, the mapping procedure very cost-intensive or even impossible to calculate, at all.