The Pearson-ICA algorithm is a mutual information-based method for blind separation of statistically independent source signals. It has been shown that the minimization of mutual information leads to iterative use of score functions, i.e. derivatives of log densities. The Pearson system allows adaptive modeling of score functions. The flexibility of the Pearson system makes it possible to model a wide range of source distributions including asymmetric distributions. The algorithm is designed especially for problems with asymmetric sources but it works for symmetric sources as well.
PearsonICA(X, n.comp = 0, row.norm = FALSE, maxit = 200, tol = 1e-04,
border.base = c(2.6, 4), border.slope = c(0, 1), verbose = FALSE,
w.init = NULL, na.rm = FALSE, whitening.only = FALSE, PCA.only = FALSE)input data. Each column contains one signal.
number of components to be extracted.
a logical value indicating whether rows of the data matrix 'X' should be standardized beforehand.
maximum number of iterations to perform
a positive scalar giving the tolerance at which the un-mixing matrix is considered to have converged.
intercept terms for the tanh boundaries. See details.
slope terms for the tanh boundaries. See details.
a logical value indicating the level of output as the algorithm runs.
initial un-mixing matrix of dimension (n.comp,n.comp). If NULL (default) then a matrix of normal r.v.'s is used.
should the rows with missing values be removed.
perform only whitening.
perform only principal component analysis.
A list containing the following components
input data
whitening matrix
estimated demixing matrix
estimated mixing matrix
separated (estimated) source signals
component means
starting value of W
maximum number of iterations allowed
convergence limit
number of iterations used
The definition of W is different from that of fastICA algorithm (version 1.1-8).
The data matrix X is considered to be a linear combination of statistically independent components, i.e. X = SA where A is a linear mixing and matrix the columns of S contain the independent components of which at most one has Gaussian distribution. The goal of ICA is to find a matrix W such that the output Y = XW is an estimate of possibly scaled and permutated source matrix S.
In order to extract the independent components/sources we search for a demixing matrix W that minimizes the mutual information of the sources. The minimization of mutual information leads to iterative use of score functions, i.e. derivatives of log densities. Pearson-ICA uses the Pearson system to model the score functions of the output Y. The parameters of the Pearson system are estimated by method of moments. To speed up the algorithm, tanh nonlinearity is used when the distribution is far from Gaussian. The parameters 'border.base' and 'border.slope' define the boundaries of the tanh area in the skewness-kurtosis plane. See Figure 2 in (Karvanen, Eriksson and Koivunen, 2000) for an illustration.
Karvanen J., Koivunen V. 2002. Blind separation methods based on Pearson system and its extensions, Signal Processing 82(4), 663--673.
Karvanen J., Eriksson J.,Koivunen V. 2000, Pearson system based method for blind separation, Proceedings of Second International Workshop on Independent Component Analysis and Blind Signal Separation (ICA2000), Helsinki, Finland, 585--590.
# NOT RUN {
S<-matrix(runif(5000),1000,5);
X<-S+S[,c(2,3,4,5,1)];
icaresults<-PearsonICA(X,verbose=TRUE)
print(icaresults$A)
# }
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