OMU: Online multiplicative update algorithm for online multi-view...

In ORKM: The Online Regularized K-Means Clustering Algorithm

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)

ORKM documentation built on April 16, 2025, 5:08 p.m.