|  | ## Principal Component Analysis (Kernel)

#### Synopsis

Performs a kernel principal component analysis (PCA).

#### Description

This operator performs a kernel-based principal components analysis (PCA). Hence, the result will be the set of data points in a non-linearly transformed space. Please note that in contrast to the usual linear PCA the kernel variant does also works for large numbers of attributes but will become slow for large number of examples.

#### Input

- example set input: expects: ExampleSet

#### Output

- example set output:
- original:
- preprocessing model:

#### Parameters

- kernel type: The kernel type
- kernel gamma: The kernel parameter gamma.
- kernel sigma1: The kernel parameter sigma1.
- kernel sigma2: The kernel parameter sigma2.
- kernel sigma3: The kernel parameter sigma3.
- kernel degree: The kernel parameter degree.
- kernel shift: The kernel parameter shift.
- kernel a: The kernel parameter a.
- kernel b: The kernel parameter b.

#### ExampleProcess