mix = gmm(dim, ncentres, covartype) mix = gmm(dim, ncentres, covartype, ppca_dim)
mix = gmm(dim, ncentres, covartype) takes
the dimension of the space dim, the number of centres in the
mixture model and the type of the mixture model, and returns a data
structure mix.
The mixture model type defines the covariance structure of each component
Gaussian:
'spherical' = single variance parameter for each component: stored as a vector
'diag' = diagonal matrix for each component: stored as rows of a matrix
'full' = full matrix for each component: stored as 3d array
'ppca' = probabilistic PCA: stored as principal components (in a 3d array
and associated variances and off-subspace noise
mix = gmm(dim, ncentres, covartype, ppca_dim) also sets the dimension of
the PPCA sub-spaces: the default value is one.
The priors are initialised to equal values summing to one, and the covariances
are all the identity matrix (or equivalent). The centres are
initialised randomly from a zero mean unit variance Gaussian. This makes use
of the MATLAB function randn and so the seed for the random weight
initialisation can be set using randn('state', s) where s is the
state value.
The fields in mix are
type = 'gmm' nin = the dimension of the space ncentres = number of mixture components covartype = string for type of variance model priors = mixing coefficients centres = means of Gaussians: stored as rows of a matrix covars = covariances of GaussiansThe additional fields for mixtures of PPCA are
U = principal component subspaces lambda = in-space covariances: stored as rows of a matrixThe off-subspace noise is stored in
covars.
mix = gmm(2, 4, 'spherical');This creates a Gaussian mixture model with 4 components in 2 dimensions. The covariance structure is a spherical model.
gmmpak, gmmunpak, gmmsamp, gmminit, gmmem, gmmactiv, gmmpost, gmmprobCopyright (c) Ian T Nabney (1996-9)