net = gtm(dimlatent, nlatent, dimdata, ncentres, rbfunc) net = gtm(dimlatent, nlatent, dimdata, ncentres, rbfunc, prior)
net = gtm(dimlatent, nlatent, dimdata, ncentres, rbfunc),
takes the dimension of the latent space dimlatent, the
number of data points sampled in the latent space nlatent, the
dimension of the data space dimdata, the number of centres in the
RBF model ncentres, the activation function for the RBF
rbfunc
and returns a data structure net. The parameters in the
RBF and GMM sub-models are set by calls to the corresponding creation routines
rbf and gmm.
The fields in net are
type = 'gtm' nin = dimension of data space dimlatent = dimension of latent space rbfnet = RBF network data structure gmmnet = GMM data structure X = sample of latent points
net = gtm(dimlatent, nlatent, dimdata, ncentres, rbfunc, prior),
sets a Gaussian zero mean prior on the
parameters of the RBF model. prior must be a scalar and represents
the inverse variance of the prior distribution. This gives rise to
a weight decay term in the error function.
gtmfwd, gtmpost, rbf, gmmCopyright (c) Ian T Nabney (1996-9)