Description Usage Arguments Details Value See Also Examples
Runs the fastICA algorithm but does not perform any matrix preprocessing operations
first (this differs from the fastICA
function in the fastICA package.) Preprocessing steps should be carried out
by running other functions, e.g. preprocessMatrix
, on the input matrix prior to
using this function.
1 2 |
X |
a numeric matrix(-like object) |
n.comp |
an integer specifying the number of components to extract |
alg.typ, fun, alpha, maxit, tol, verbose |
See |
w.init |
integer value to reset random number generator with (see Details) |
max.attempts |
maximum number of attempts at convergence to make |
This function runs the fastICA algorithm on the input matrix but it does so without running
any preprocessing operations (such as centering and scaling on the rows and/or columns) on the input
matrix first. This allows alternative preprocessing methods (such as scaleMatrix
)
to be applied to the input matrix, which can improve results. The preprocessMatrix
function
may be run on the input matrix to carryout typical preprocessing steps, such as column centering.
Note: The default parameter values in preprocessMatrix
match those of fastICA
,
thus, running preprocessMatrix
followed by dex.fastICA
with default parameter values is equivalent to running fastICA
.
If w.init
is not NULL, its value will be used to reset the random number generator
(using set.seed
) prior to randomly generarting the initial W ('unmixing') matrix.
This differs from fastICA
, as w.init there can be either NULL or a matrix.
This modification to w.init was made in order to make reproducing results easier.
Another difference between this algorithm and fastICA
is that multiple attempts
at convergence can be made if convergence fails in the first attempt. The maximum number of attempts
that will be made is specified by the max.attempts
parameter. The maximum number of iterations in
each attempt, specified by maxit
, is increased by 1.5x in each subsequent attempt. If convergence
fails, the ICA solution found at the last iteration of the last attempt will be returned as an approximate
solution.
A list with the following elements:
The estimated source matrix
The estimated mixing matrix
The number of attempts made at finding a solution
The number of iterations made during the last attempt
A logical value indicating whether convergence was reached
fastICA
, preprocessMatrix
,
predictModules
1 2 3 4 | x = matrix(rnorm(100), 10, 10)
x = preprocessMatrix(x)
m = dexFastICA(x, n.comp = 3)
m$converged
|
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