|  | ## Independent Component Analysis

#### Synopsis

Performs an independent component analysis (ICA).

#### Description

This operator performs the independent componente analysis (ICA). Implementation of the FastICA-algorithm of Hyvaerinen und Oja. The operator outputs a `FastICAModel`. With the `ModelApplier` you can transform the features.

#### Input

- example set input: expects: ExampleSet

#### Output

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

#### Parameters

- dimensionality reduction: Indicates which type of dimensionality reduction should be applied
- number of components: Keep this number of components.
- algorithm type: If 'parallel' the components are extracted simultaneously, 'deflation' the components are extracted one at a time
- function: The functional form of the G function used in the approximation to neg-entropy
- alpha: constant in range [1, 2] used in approximation to neg-entropy when fun="logcosh"
- row norm: Indicates whether rows of the data matrix should be standardized beforehand.
- max iteration: maximum number of iterations to perform
- tolerance: A positive scalar giving the tolerance at which the un-mixing matrix is considered to have converged.
- use local random seed: Indicates if a local random seed should be used.
- local random seed: Specifies the local random seed

#### ExampleProcess