R: ICA via FastICA Algorithm

icafast {ica}

R Documentation

ICA via FastICA Algorithm

Description

Computes ICA decomposition using Hyvarinen's (1999) FastICA algorithm with various options.

Usage

icafast(X,nc,center=TRUE,maxit=100,tol=1e-6,Rmat=diag(nc),
        alg=c("par","def"),fun=c("logcosh","exp","kur"),alpha=1)

Arguments

`X`	Data matrix with `n` rows (samples) and `p` columns (variables).
`nc`	Number of components to extract.
`center`	If `TRUE`, columns of `X` are mean-centered before ICA decomposition.
`maxit`	Maximum number of algorithm iterations to allow.
`tol`	Convergence tolerance.
`Rmat`	Initial estimate of the `nc`-by-`nc` orthogonal rotation matrix.
`alg`	Algorithm to use: `alg="par"` to estimate all `nc` components in parallel (default) or `alg="def"` for deflation estimation (i.e., projection pursuit).
`fun`	Contrast function to use for negentropy approximation.
`alpha`	Tuning parameter for "logcosh" contrast function (1 <= `alpha` <= 2).

Details

ICA Model The ICA model can be written as X=tcrossprod(S,M)+E, where columns of S contain the source signals, M is the mixing matrix, and columns of E contain the noise signals. Columns of X are assumed to have zero mean. The goal is to find the unmixing matrix W such that columns of S=tcrossprod(X,W) are independent as possible.

Whitening Without loss of generality, we can write M=P%*%R where P is a tall matrix and R is an orthogonal rotation matrix. Letting Q denote the pseudoinverse of P, we can whiten the data using Y=tcrossprod(X,Q). The goal is to find the orthongal rotation matrix R such that the source signal estimates S=Y%*%R are as independent as possible. Note that W=crossprod(R,Q).

FastICA The FastICA algorithm finds the orthogonal rotation matrix R that (approximately) maximizes the negentropy of the estimated source signals. Negentropy is approximated using

J(s) = [E\{G(s)\}-E\{G(z)\} ]^2

where E denotes the expectation, G is the contrast function, and z is a standard normal variable. See Hyvarinen (1999) for specifics of fixed-point algorithm.

Value

`S`	Matrix of source signal estimates (`S=Y%*%R`).
`M`	Estimated mixing matrix.
`W`	Estimated unmixing matrix (`W=crossprod(R,Q)`).
`Y`	Whitened data matrix.
`Q`	Whitening matrix.
`R`	Orthogonal rotation matrix.
`vafs`	Variance-accounted-for by each component.
`iter`	Number of algorithm iterations.
`alg`	Algorithm used (same as input).
`fun`	Contrast function (same as input).
`alpha`	Tuning parameter (same as input).

Author(s)

Nathaniel E. Helwig <helwig@umn.edu>

References

Helwig, N.E. & Hong, S. (2013). A critique of Tensor Probabilistic Independent Component Analysis: Implications and recommendations for multi-subject fMRI data analysis. Journal of Neuroscience Methods, 213, 263-273.

Hyvarinen, A. (1999). Fast and robust fixed-point algorithms for independent component analysis. IEEE Transactions on Neural Networks, 10, 626-634.

Examples

##########   EXAMPLE 1   ##########

# generate noiseless data (p==r)
set.seed(123)
nobs <- 1000
Amat <- cbind(icasamp("a","rnd",nobs),icasamp("b","rnd",nobs))
Bmat <- matrix(2*runif(4),2,2)
Xmat <- tcrossprod(Amat,Bmat)

# ICA via FastICA with 2 components
imod <- icafast(Xmat,2)
acy(Bmat,imod$M)
congru(Amat,imod$S)



##########   EXAMPLE 2   ##########

# generate noiseless data (p!=r)
set.seed(123)
nobs <- 1000
Amat <- cbind(icasamp("a","rnd",nobs),icasamp("b","rnd",nobs))
Bmat <- matrix(2*runif(200),100,2)
Xmat <- tcrossprod(Amat,Bmat)

# ICA via FastICA with 2 components
imod <- icafast(Xmat,2)
congru(Amat,imod$S)



##########   EXAMPLE 3   ##########

# generate noisy data (p!=r)
set.seed(123)
nobs <- 1000
Amat <- cbind(icasamp("a","rnd",nobs),icasamp("b","rnd",nobs))
Bmat <- matrix(2*runif(200),100,2)
Emat <- matrix(rnorm(10^5),1000,100)
Xmat <- tcrossprod(Amat,Bmat)+Emat

# ICA via FastICA with 2 components
imod <- icafast(Xmat,2)
congru(Amat,imod$S)

[Package ica version 1.0-1 Index]