Variational Bayesian inference for unsupervised clustering, mixture of independent Gaussians

Variational Bayesian inference for unsupervised clustering, mixture of independent Gaussians

vimixSelGauss(X, K, prior, init = "kmeans", tol = 1e-19,
  maxiter = 2000, verbose = F)

Arguments

X

NxD data matrix.
K

(Maximum) number of clusters.
prior

Prior parameters (optional).
init

Initialisation method (optional). If it is a vector, it is interpreted as the vector of initial cluster allocations. If it is a string, it is interpreted as the name of the clustering algorithm used for the initialisation (only "kmeans" and "random") available at the moment).
tol

Tolerance on lower bound. Default is 10e-20.
maxiter

Maximum number of iterations of the VB algorithm. Default is 2000.
verbose

Boolean flag which, if TRUE, prints the iteration numbers. Default is FALSE.

Value

A list containing L, the lower bound at each step of the algorithm, label, a vector containing the cluster labels, model, a list containing the trained model structure.

References

Bishop, C.M., 2006. Pattern recognition and machine learning. Springer.

Examples

library(mvtnorm)
data <- rbind(rmvnorm(100,c(-3,0)), rmvnorm(100,c(3,0)))
output <- vimixIndGauss(data, 2)