linear_ICA: Independent Component Analysis

In Rdimtools: Dimension Reduction and Estimation Methods

Independent Component Analysis

Description

do.ica is an R implementation of FastICA algorithm, which aims at finding weight vectors that maximize a measure of non-Gaussianity of projected data. FastICA is initiated with pre-whitening of the data. Single and multiple component extraction are both supported. For more detailed information on ICA and FastICA algorithm, see this Wikipedia page.

Usage

do.ica(
  X,
  ndim = 2,
  type = "logcosh",
  tpar = 1,
  sym = FALSE,
  tol = 1e-06,
  redundancy = TRUE,
  maxiter = 100
)

Arguments

X	an (n\times p) matrix or data frame whose rows are observations and columns represent independent variables.
ndim	an integer-valued target dimension.
type	nonquadratic function, one of "logcosh","exp", or "poly" be chosen.
tpar	a numeric parameter for logcosh and exp parameters that should be close to 1.
sym	a logical value; FALSE for not using symmetric decorrelation, TRUE otherwise.
tol	stopping criterion for iterative update.
redundancy	a logical value; TRUE for removing NA values after prewhitening, FALSE otherwise.
maxiter	maximum number of iterations allowed. Y an (n\times ndim) matrix whose rows are embedded observations. trfinfo a list containing information for out-of-sample prediction. projection a (p\times ndim) whose columns are basis for projection.

Details

In most of ICA literature, we have

S = X*W

where W is an unmixing matrix for the given data X. In order to preserve consistency throughout our package, we changed the notation; Y a projected matrix for S, and projection for unmixing matrix W.

Author(s)

Kisung You

References

\insertRef

hyvarinen_independent_2001Rdimtools

Examples

## use iris dataset
data(iris)
set.seed(100)
subid = sample(1:150,50)
X     = as.matrix(iris[subid,1:4])
lab   = as.factor(iris[subid,5])

## 1. use logcosh function for transformation
output1 <- do.ica(X,ndim=2,type="logcosh")

## 2. use exponential function for transformation
output2 <- do.ica(X,ndim=2,type="exp")

## 3. use polynomial function for transformation
output3 <- do.ica(X,ndim=2,type="poly")

## Visualize three different projections
opar <- par(no.readonly=TRUE)
par(mfrow=c(1,3))
plot(output1$Y, col=lab, pch=19, main="ICA::logcosh")
plot(output2$Y, col=lab, pch=19, main="ICA::exp")
plot(output3$Y, col=lab, pch=19, main="ICA::poly")
par(opar)

Rdimtools documentation built on Dec. 28, 2022, 1:44 a.m.