SFtools (version 0.1.0)

UfsCov_par: UfsCov algorithm for unsupervised feature selection

Description

Applies the UfsCov algorithm based on the space filling concept, by using a sequatial forward search (SFS).This function offers a parellel computing.

Usage

UfsCov_par(data, ncores=2)

Arguments

data

Data of class: matrix or data.frame.

ncores

Number of cores to use (by default: ncores=2).

Value

A list of two elements:

• CovD a vector containing the coverage measure of each step of the SFS.

• IdR a vector containing the added variables during the selection procedure.

Details

Since the algorithm is based on pairwise distances, and according to the computing power of your machine, large number of data points needs more memory. See UfsCov_ff for memory efficient storage of large data on disk and fast access (by using the ff and the ffbase packages).

References

M. Laib and M. Kanevski (2017). Unsupervised Feature Selection Based on Space Filling Concept, arXiv:1706.08894.

See Also

UfsCov, UfsCov_ff

Examples

N <- 800
dat<-Infinity(N)
Results<- UfsCov_par(dat,ncores=2)

cou<-colnames(dat)
nom<-cou[Results[[2]]]
par(mfrow=c(1,1), mar=c(5,5,2,2))
names(Results[[1]])<-cou[Results[[2]]]
plot(Results[[1]] ,pch=16,cex=1,col="blue", axes = FALSE,
xlab = "Added Features", ylab = "Coverage measure")
lines(Results[[1]] ,cex=2,col="blue")
grid(lwd=1.5,col="gray" )
box()
axis(2)
axis(1,1:length(nom),nom)
which.min(Results[[1]])



N<-5000
dat<-Infinity(N)

## Little comparison:
system.time(Uf<-UfsCov(dat))
system.time(Uf.p<-UfsCov_par(dat, ncores = 4))