Drsvd {FPCdpca}

Distributed random svd

Description

Distributed random svd is a technology that applies random SVD to distributed computing environments.

Usage

Drsvd(data,K, nk,m,q,k)

Arguments

data

sparse random projection matrix.

K

the number of distributed nodes.

nk

the size of subsets.

m

the dimension of variables.

q

number of additional power iterations.

k

the desired target rank.

Value

MSEXrsvd

The MSE value of Xrsvd

MSEvrsvd

The MSE value of vrsvd

MSESrsvd

The MSE value of Srsvd

kopt

The size of optimal subset

Examples

K=20; nk=50; nr=10; p=8; m=5; q=5;k=4;n=K*nk;
data=X=matrix(rexp(n*p,0.8),ncol=p)
#data=matrix(c(rnorm((n-nr)*p,0,1),rpois(nr*p,100)),ncol=p)
#data=X=matrix(rpois((n-nr)*p,1),ncol=p); rexp(nr*p,1); rchisq(10000, df = 5);
#data=X=matrix(rexp(n*p,0.8),ncol=p)
Drsvd(data=data,K=K,nk=nk,m=m,q=q,k=k)