DMOEM {DEM}

The DMOEM is an overrelaxation algorithm in distributed manner, which is used to solve the parameter estimation of multivariate Gaussian mixture model.

Description

The DMOEM is an overrelaxation algorithm in distributed manner, which is used to solve the parameter estimation of multivariate Gaussian mixture model.

Usage

DMOEM(
  y,
  M,
  seed,
  alpha0,
  mu0,
  sigma0,
  MOEMalpha0,
  MOEMmu0,
  MOEMsigma0,
  omega,
  i,
  epsilon
)

Arguments

y

is a data matrix

M

is the number of subsets

seed

is the recommended way to specify seeds

alpha0

is the initial value of the mixing weight under the EM algorithm

mu0

is the initial value of the mean under the EM algorithm

sigma0

is the initial value of the covariance under the EM algorithm

MOEMalpha0

is the initial value of the mixing weight under the MOEM algorithm

MOEMmu0

is the initial value of the mean under the MOEM algorithm

MOEMsigma0

is the initial value of the covariance under the MOEM algorithm

omega

is the overrelaxation factor

i

is the number of iterations

epsilon

is the threshold value

Value

DMOEMalpha,DMOEMmu,DMOEMsigma,DMOEMtime

Examples

library(mvtnorm)
alpha1= c(rep(1/4,4)) 
mu1=matrix(0,nrow=4,ncol=4) 
for (k in 1:4){
mu1[4,]=c(runif(4,(k-1)*3,k*3)) 
}
sigma1=list()
for (k in 1:4){
sigma1[[k]]= diag(4)*0.1
}
y= matrix(0,nrow=200,ncol=4) 
for(k in 1:4){
y[c(((k-1)*200/4+1):(k*200/4)),] = rmvnorm(200/4,mu1[k,],sigma1[[k]]) 
}
M=5
seed=123
alpha0= alpha1
mu0=mu1
sigma0=sigma1
MOEMalpha0= alpha1
MOEMmu0=mu1
MOEMsigma0=sigma1
omega=0.15
i=10
epsilon=0.005
DMOEM(y,M,seed,alpha0,mu0,sigma0,MOEMalpha0,MOEMmu0,MOEMsigma0,omega,i,epsilon)