## File: ica.Rd

package info (click to toggle)
r-cran-e1071 1.7-3-1
 1234567891011121314151617181920212223242526272829303132333435363738394041424344454647484950 \name{ica} \alias{ica} \alias{plot.ica} \alias{print.ica} \title{Independent Component Analysis} \usage{ ica(X, lrate, epochs=100, ncomp=dim(X)[2], fun="negative") } \arguments{ \item{X}{The matrix for which the ICA is to be computed} \item{lrate}{learning rate} \item{epochs}{number of iterations} \item{ncomp}{number of independent components} \item{fun}{function used for the nonlinear computation part} } \description{ This is an R-implementation of the Matlab-Function of Petteri.Pajunen@hut.fi. For a data matrix X independent components are extracted by applying a nonlinear PCA algorithm. The parameter \code{fun} determines which nonlinearity is used. \code{fun} can either be a function or one of the following strings "negative kurtosis", "positive kurtosis", "4th moment" which can be abbreviated to uniqueness. If \code{fun} equals "negative (positive) kurtosis" the function tanh (x-tanh(x)) is used which provides ICA for sources with negative (positive) kurtosis. For \code{fun == "4th moments"} the signed square function is used. } \value{ An object of class \code{"ica"} which is a list with components \item{weights}{ICA weight matrix} \item{projection}{Projected data} \item{epochs}{Number of iterations} \item{fun}{Name of the used function} \item{lrate}{Learning rate used} \item{initweights}{Initial weight matrix} } \references{ Oja et al., Learning in Nonlinear Constrained Hebbian Networks'', in Proc. ICANN-91, pp. 385--390. Karhunen and Joutsensalo, Generalizations of Principal Component Analysis, Optimization Problems, and Neural Networks'', Neural Networks, v. 8, no. 4, pp. 549--562, 1995. } \note{Currently, there is no reconstruction from the ICA subspace to the original input space.} \author{Andreas Weingessel} \keyword{multivariate}