MICA: Multimodal independent component analysis

In iTensor: ICA-Based Matrix/Tensor Decomposition

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)

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