What is the role of kernel choice in kernel PCA?

Kernel choice defines how data is mapped into higher-dimensional feature space.

Kernel PCA uses kernel functions to implicitly transform data into a higher-dimensional space where linear PCA is applied. The kernel determines the type of nonlinear structure captured. RBF kernels capture smooth nonlinearities, polynomial kernels capture interaction terms, and sigmoid kernels resemble neural activations.