iTensor (version 1.0.2)

ICA2: Independent Component Analysis (Modern 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

ICA2(
  X,
  J,
  algorithm = c("JADE", "AuxICA1", "AuxICA2", "IPCA", "SIMBEC", "AMUSE", "SOBI", "FOBI",
    "ProDenICA", "RICA"),
  num.iter = NULL,
  thr = 1e-10,
  r_list = NULL,
  omega_for_each_r = NULL,
  a_r_for_each_r = NULL,
  tau_list = NULL,
  num_bins = NULL,
  alpha = NULL,
  num_epoch = NULL,
  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: "JADE")

num.iter

The number of iteration

thr

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

r_list

List of r-th order cumulants used in SIMBEC (Default: NULL)

omega_for_each_r

Weight vector of r_list used in SIMBEC (Default: NULL)

a_r_for_each_r

Parameter vector to specify the shape of partial activation function in SIMBEC (Default: NULL)

tau_list

List of lags to consider the auto-correlation used in AMUSE and SOBI (Default: NULL)

num_bins

Number of bins for histgram in ProDenICA (Default: NULL)

alpha

Learning rate used for gradient descent in RICA (Default: NULL)

num_epoch

Number of epoch used for gradient descent in RICA (Default: NULL)

verbose

Verbose option

Examples

ICA2