fourierTransPredict: Prediction based on random Fourier transformation
In kernDeepStackNet: Kernel Deep Stacking Networks

Description Usage Arguments Value Note Author(s) References See Also Examples

Based on prior given weights, the estimated Fourier transformation is applied to new data.

1	fourierTransPredict(newx, rW)

`newx`	New data design matrix.
`rW`	Prior drawn random weight matrix.

Numeric transformed data matrix with dimension 2*Dim x n.

This function is not intended to be called directly by the user. Should only be used by experienced users, who want to customize the model. It is called in the estimation process of the kernel deep stacking network, e.g. fitKDSN.

Thomas Welchowski welchow@imbie.meb.uni-bonn.de

Po-Seng Huang and Li Deng and Mark Hasegawa-Johnson and Xiaodong He, (2013), Random Features for kernel deep convex network, Proceedings IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

randomFourierTrans

# Generate data matrix
set.seed(50)
X <- matrix(rnorm(100*3), ncol=3)

# Apply a random Fourier transformation of higher dimension
rft <- randomFourierTrans(X=X, Dim=3, sigma=1, seedW=0)

# New data matrix
set.seed(100)
Xnew <- matrix(rnorm(100*3), ncol=3)

# Apply same Fourier transformation on new data
newZ <- fourierTransPredict(newx=Xnew, rW=rft$rW)
head(newZ)