MICA {iTensor}

Multimodal independent component analysis

Description

The input datasets are assumed to be two matrices sharing the column space. MICA decomposes the matrices simutanously and extracts the components that maximizes the mutual information between the components.

Usage

MICA(
  X,
  Y,
  J,
  eta = 1000 * 1e-04,
  verbose = FALSE,
  mu = 50 * 1e-04,
  gamma_ts = 1
)

Arguments

X

A matrix sharing the column space with Y (??? x N)

Y

A matrix sharing the column space with X (??? x N)

J

The rank parameter to decompose the matrices

eta

A learning rate parameter of stochastic gradient descent

verbose

Verbose option

mu

A learning rate parameter of stochastic gradient descent

gamma_ts

Weighting factor for dependence on independence

Value

A list containing the result of the decomposition

Examples

X <- array(runif(10*20), dim=c(10,20))
Y <- array(runif(15*20), dim=c(15,20))
J <- 20
out <- MICA(X, Y, J=J)