GroupICA: Group Independent Component Analysis (GroupICA)

In iTensor: ICA-Based Matrix/Tensor Decomposition

Group Independent Component Analysis (GroupICA)

Description

The input data is assumed to be a list containing multiple matrices, which share common column.

Usage

GroupICA(
  Xs,
  J1,
  J2 = J1,
  algorithm = c("pooled", "Calhoun2009", "Pfister2018"),
  ica.algorithm = c("FastICA", "InfoMax", "ExtInfoMax", "JADE", "AuxICA1", "AuxICA2",
    "IPCA", "SIMBEC", "AMUSE", "SOBI", "FOBI", "ProDenICA", "RICA"),
  num.iter = 30,
  thr = 1e-10,
  verbose = FALSE
)

Arguments

Xs	A list containing multiple matrices
J1	Rank parameter to decompose
J2	Rank parameter used in Calhoun2009
algorithm	Pool algorithm to merge multiple ICA results (Default: pooled)
ica.algorithm	The decomposition algorithm (Default: "FastICA")
num.iter	The number of iterations
thr	The threshold to terminate the iteration (Default: 1E-10)
verbose	Verbose option

Value

A list containing the result of the decomposition

Examples

X1 <- matrix(runif(100*200), nrow=100, ncol=200)
X2 <- matrix(runif(150*200), nrow=150, ncol=200)
Xs <- list(X1=X1, X2=X2)
out <- GroupICA(Xs, J1=5)

iTensor documentation built on April 28, 2023, 9:11 a.m.