Independent Component Analysis
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Performs an independent component analysis (ICA).
Contents |
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
Range: none, fixed number; default: none - number of components:
Keep this number of components.
Range: integer; 1-+? - algorithm type:
If 'parallel' the components are extracted simultaneously, 'deflation' the components are extracted one at a time
Range: deflation, parallel; default: deflation - function:
The functional form of the G function used in the approximation to neg-entropy
Range: logcosh, exp; default: logcosh - alpha:
constant in range [1, 2] used in approximation to neg-entropy when fun="logcosh"
Range: real; 1.0-2.0 - row norm:
Indicates whether rows of the data matrix should be standardized beforehand.
Range: boolean; default: false - max iteration:
maximum number of iterations to perform
Range: integer; 0-+?; default: 200 - tolerance:
A positive scalar giving the tolerance at which the un-mixing matrix is considered to have converged.
Range: real; 0.0-+? - use local random seed:
Indicates if a local random seed should be used.
Range: boolean; default: false - local random seed:
Specifies the local random seed
Range: integer; 1-+?; default: 1992