Package smile.ica
package smile.ica
Independent Component Analysis (ICA). ICA is a computational method
for separating a multivariate signal into additive components. This
is done by assuming that at most one subcomponent is a non-Gaussian
signals and that the subcomponents are statistically independent of
each other. ICA is a special case of blind source separation. A common
example application is the "cocktail party problem" of listening in
on one person's speech in a noisy room.
FastICA is an efficient algorithm for ICA invented by Aapo Hyvärinen. Using maximum entropy approximations of differential entropy, FastICA introduce a family of new contrast (objective) functions for ICA. These contrast functions enable both the estimation of the whole decomposition by minimizing mutual information, and estimation of individual independent components as projection pursuit directions.
The contrast functions must be a non-quadratic non-linear function that has second-order derivative.
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ClassesClassDescriptionThe contrast function when the independent components are highly super-Gaussian, or when robustness is very important.Independent Component Analysis (ICA) is a computational method for separating a multivariate signal into additive components.The kurtosis of the probability density function of a signal.A good general-purpose contrast function for ICA.