OMU {ORKM}

Online multiplicative update algorithm for online multi-view data clustering

Description

This algorithm integrates the multiplicative normalization factor as an additional term in the original additivity update rule, which usually has approximately opposite direction. Thus, the improved iteration rule can be easily converted to a multiplicative version. After each iteration After each iteration, non-negativity is maintained.

Usage

OMU(X,K,V,chushi,yita,r,max.iter,epsilon,truere,method=0)

Arguments

X

data matrix

K

number of cluster

V

number of view

chushi

the initial value

yita

the regularized parameter

r

banlance parameter

max.iter

max iter

epsilon

epsilon

truere

true cluster result

method

caculate the index of NMI

Value

NMI,result,M

Examples

 yita=0.5;V=2;chushi=100;K=3;r=0.5;max.iter=10;n1=n2=n3=70;epsilon=1
 X1<-rnorm(n1,20,2);X2<-rnorm(n2,25,1.5);X3<-rnorm(n3,30,2) 
 Xv<-c(X1,X2,X3)
 data<-matrix(Xv,n1+n2+n3,2)
 data[1:70,2]<-1;data[71:140,2]<-2;data[141:210,2]<-3
 truere=data[,2]
 X<-matrix(data[,1],n1+n2+n3,1) 
 lamda1<-0.2;lamda2<-0.8
 lamda<-matrix(c(lamda1,lamda2),nrow=1,ncol=2)
 sol.svd <- svd(lamda)
 U1<-sol.svd$u
 D1<-sol.svd$d
 V1<-sol.svd$v
 C1<-t(U1)%*%t(X)
 Y1<-C1/D1
 view<-V1%*%Y1
 view1<-matrix(view[1,])
 view2<-matrix(view[2,])
 X1<-matrix(view1,n1+n2+n3,1)
 X2<-matrix(view2,n1+n2+n3,1)
 OMU(X=X1,K=K,V=V,chushi=chushi,yita=yita,r=r,max.iter=max.iter,
epsilon=epsilon,truere=truere,method=0)