DMOEM: The DMOEM is an overrelaxation algorithm in distributed...

In DEM: The Distributed EM Algorithms in Multivariate Gaussian Mixture Models

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)

DEM documentation built on May 14, 2022, 9:05 a.m.