FastIca Class

public sealed class FastIca : TransformationModel<double>

Public NotInheritable Class FastIca
	Inherits TransformationModel(Of Double)

public ref class FastIca sealed : public TransformationModel<double>

[<SealedAttribute>]
type FastIca = 
    class
        inherit TransformationModel<float>
    end

Inheritance

Object  →  Model  →  TransformationModel<Double>  →  FastIca

Independent Component Analysis (ICA) is a computational method for separating a multivariate signal into additive subcomponents that are statistically independent and non-Gaussian. ICA is widely used in signal processing, data analysis, and machine learning for tasks such as blind source separation, feature extraction, and noise reduction.

This class provides an implementation of the FastICA algorithm, which is an efficient and popular method for performing ICA. FastICA can extract independent components from observed data by maximizing non-Gaussianity using fixed-point iteration. The algorithm supports both parallel (symmetric) and deflation (sequential) extraction modes.

Features of this implementation include:

• Support for multiple contrast (nonlinearity) functions: LogCosh, Exponential, and Cubic.
• Configurable number of components to extract.
• Flexible whitening options: skip, arbitrary variance, and unit variance (matching scikit-learn semantics).
• Sign correction to ensure consistent orientation of components.
• Access to mixing and unmixing matrices, source estimates, whitening matrices, and convergence reports.
• API compatibility with common ICA libraries and test suites.

Usage:

var ica = new FastIca(dataFrame);
ica.NumberOfComponents = 3;
ica.ContrastFunction = FastIcaContrastFunction.LogCosh;
ica.Fit();
var sources = ica.Sources;
var mixing = ica.MixingMatrix;

References:

• Hyvärinen, A., & Oja, E. (2000). Independent component analysis: algorithms and applications. Neural Networks, 13(4-5), 411-430.
• Scikit-learn: https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.FastICA.html

Reference