gtm_pci_beta {gtm}R Documentation

Returns a weight matrix initialised using principal components.

Description

The returned weight matrix maps the mean of the latent variable to the mean of the target variable, and the L-dimensional latent variable variance to the variance of the target data along its L first principal components.

An initial value for beta is also calculated, based on the noise of the data (the "L+1"th eigenvalue) and the interdistances between Gaussian mixture centres in the data space.

Usage

gtm_pci_beta(T, X, FI)

Arguments

T target distribution sample; one data point per row; N-by-D
X the latent distribution sample, K-by-L
FI basis functions' activation when fed the latent data, X, plus a bias, K-by-(M+1)

Value

List with elements

W the initialised weight matrix, (K+1)-by-D
beta the initial beta value, scalar.

Note

The first dimension (column) of X will map to the first principal component, the second dimension (column) of X will map to the second principal component, and so on. This may be of importance for the choice of sampling density along the different dimensions of X, if it differs between different dimensions.

See Also

gtm_ri, gtm_pci

Examples

  MU = matrix(c(0,1), ncol = 1)
  sigma = 1
  X = gtm_pts(8)
  FI = gtm_gbf(MU, sigma, X)
  t = matrix((1:40)/5, ncol=1)
  T = cbind(t, sin(t))
  gtm_pci_beta(T, X, FI)


[Package gtm version 1.0 Index]