Static Public Member Functions
static InfomaxResult	compute (const Eigen::MatrixXd &matData, int nComponents=-1, int maxIterations=200, double learningRate=0.001, double tolerance=1e-7, bool extendedMode=true, unsigned int seed=0)

Detailed Description

Extended Infomax ICA (Lee et al., 1999).

Performs Independent Component Analysis using the extended infomax algorithm, which can separate both super-Gaussian and sub-Gaussian sources.

Definition at line 76 of file extended_infomax.h.

Member Function Documentation

InfomaxResult ExtendedInfomax::compute	(	const Eigen::MatrixXd &	matData,
		int	nComponents = -1,
		int	maxIterations = 200,
		double	learningRate = 0.001,
		double	tolerance = 1e-7,
		bool	extendedMode = true,
		unsigned int	seed = 0 )

static

Compute ICA decomposition using the extended infomax algorithm.

Parameters

[in]	matData	Input data matrix (n_channels x n_times), should be mean-removed.
[in]	nComponents	Number of components to extract (-1 for n_channels).
[in]	maxIterations	Maximum number of iterations.
[in]	learningRate	Learning rate for weight updates.
[in]	tolerance	Convergence tolerance.
[in]	extendedMode	If true, use extended mode (sub- and super-Gaussian).
[in]	seed	Random seed (0 for no seeding).

Definition at line 65 of file extended_infomax.cpp.

The documentation for this class was generated from the following files: