iTensor (version 1.0.2)

ICA: Independent Component Analysis (Classic Methods)

Description

The input data is assumed to be a matrix. ICA decomposes the matrix and extract the components that are statistically independent each other.

Usage

ICA(
  X,
  J,
  algorithm = c("FastICA", "InfoMax", "ExtInfoMax"),
  num.iter = 100,
  thr = 1e-10,
  nonlinear_func = c("tanh", "exp", "kurtosis"),
  learning_rate = 1,
  verbose = FALSE
)

Value

A list containing the result of the decomposition

Arguments

X

A matrix

J

Rank parameter to decompose

algorithm

The decomposition algorithm (Default: "FastICA")

num.iter

The number of iteration

thr

The threshold to terminate the iteration (Default: 1E-10)

nonlinear_func

The function used in FastICA (Default: "tanh")

learning_rate

The learning rate used in InfoMax or ExtInfoMax

verbose

Verbose option

Examples

X <- matrix(runif(100*200), nrow=100, ncol=200)
J <- 5
out.FastICA <- ICA(X, J=J, algorithm="FastICA")
out.InfoMax <- ICA(X, J=J, algorithm="InfoMax")
out.ExtInfoMax <- ICA(X, J=J, algorithm="ExtInfoMax")