mlpack (version 3.4.2)

radical: RADICAL

Description

An implementation of RADICAL, a method for independent component analysis (ICA). Given a dataset, this can decompose the dataset into an unmixing matrix and an independent component matrix; this can be useful for preprocessing.

Usage

radical(
  input,
  angles = NA,
  noise_std_dev = NA,
  objective = FALSE,
  replicates = NA,
  seed = NA,
  sweeps = NA,
  verbose = FALSE
)

Arguments

input

Input dataset for ICA (numeric matrix).

angles

Number of angles to consider in brute-force search during Radical2D. Default value "150" (integer).

noise_std_dev

Standard deviation of Gaussian noise. Default value "0.175" (numeric).

objective

If set, an estimate of the final objective function is printed. Default value "FALSE" (logical).

replicates

Number of Gaussian-perturbed replicates to use (per point) in Radical2D. Default value "30" (integer).

seed

Random seed. If 0, 'std::time(NULL)' is used. Default value "0" (integer).

sweeps

Number of sweeps; each sweep calls Radical2D once for each pair of dimensions. Default value "0" (integer).

verbose

Display informational messages and the full list of parameters and timers at the end of execution. Default value "FALSE" (logical).

Value

A list with several components:

output_ic

Matrix to save independent components to (numeric matrix).

output_unmixing

Matrix to save unmixing matrix to (numeric matrix).

Details

An implementation of RADICAL, a method for independent component analysis (ICA). Assuming that we have an input matrix X, the goal is to find a square unmixing matrix W such that Y = W * X and the dimensions of Y are independent components. If the algorithm is running particularly slowly, try reducing the number of replicates.

The input matrix to perform ICA on should be specified with the "input" parameter. The output matrix Y may be saved with the "output_ic" output parameter, and the output unmixing matrix W may be saved with the "output_unmixing" output parameter.

Examples

# NOT RUN {
# For example, to perform ICA on the matrix "X" with 40 replicates, saving
# the independent components to "ic", the following command may be used: 

# }
# NOT RUN {
output <- radical(input=X, replicates=40)
ic <- output$output_ic
# }