UTILSLIB::PicardIca Class Reference

Independent Component Analysis using the Picard algorithm. More...

#include <picard_ica.h>

Static Public Member Functions
static IcaResult	run (const Eigen::MatrixXd &matData, int nComponents=-1, int maxIter=200, double tol=1e-7, int lbfgsMemory=7, int randomSeed=42)

Detailed Description

Independent Component Analysis using the Picard algorithm.

Picard uses a preconditioned L-BFGS strategy to minimise the mutual information of the components. Compared to FastICA it typically converges in fewer iterations and handles badly conditioned data more robustly.

Definition at line 56 of file picard_ica.h.

Member Function Documentation

run()

IcaResult PicardIca::run ( const Eigen::MatrixXd & matData, int nComponents = -1, int maxIter = 200, double tol = 1e-7, int lbfgsMemory = 7, int randomSeed = 42 )

static

Run the Picard ICA algorithm.

Parameters

[in]	matData	Input data (n_channels x n_samples).
[in]	nComponents	Number of components to extract (-1 = all channels).
[in]	maxIter	Maximum number of iterations (default 200).
[in]	tol	Convergence tolerance (default 1e-7).
[in]	lbfgsMemory	L-BFGS memory length (default 7).
[in]	randomSeed	Seed for initial weight randomisation (default 42).

Returns

IcaResult with mixing/unmixing matrices and source time series.

Definition at line 71 of file picard_ica.cpp.

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

• src/libraries/dsp/picard_ica.h
• src/libraries/dsp/picard_ica.cpp