DLFM (version 0.2.2)

DIPC: Distributed Incremental Principal Component Analysis (DIPC)

Description

Apply IPC in a distributed manner across K nodes.

Usage

DIPC(data, m, eta, K)

Value

List with per-node results and aggregated averages.

Arguments

data

Matrix of input data (n × p).

m

Number of principal components.

eta

Proportion of initial batch to total data within each node.

K

Number of nodes (distributed splits).

Examples

library(LaplacesDemon)
library(MASS)
n=1000
p=10
m=5
mu=t(matrix(rep(runif(p,0,1000),n),p,n))
mu0=as.matrix(runif(m,0))
sigma0=diag(runif(m,1))
F=matrix(mvrnorm(n,mu0,sigma0),nrow=n)
A=matrix(runif(p*m,-1,1),nrow=p)
lanor <- rlaplace(n*p,0,1)
epsilon=matrix(lanor,nrow=n)
D=diag(t(epsilon)%*%epsilon)
data=mu+F%*%t(A)+epsilon
results <- DIPC(data, m, eta=0.8, K=5)