sslLLGC: Local and Global Consistency

In SSL: Semi-Supervised Learning

Description

Local and Global Consistency

Usage

sslLLGC(xl, yl, xu, dist.type = "Euclidean", alpha = 0.01, gamma = 1,
  iter = 1000)

Arguments

xl	a n * p matrix or data.frame of labeled data
yl	a n X C matrix representing labels of n observations in C classes.If observation i belongs to class j, then yl(i,j)=1, and other elements in the same row equal 0.
xu	a m * p matrix or data.frame of unlabeled data.
dist.type	character string; this parameter controls the type of distance measurement.(see dist or pr_DB).
alpha	a numeric parameter controls convergence rate.
gamma	a numeric parameter in the affinity matrix
iter	the number of iteration.

Value

a m * 1 integer vector representing the predicted labels of unlabeled data.

Author(s)

Junxiang Wang

References

Zhou, D., Bousquet, O., Lal, T., Weston, J. and Scholkopf, B. (2004). Learning with local and global consistency.

See Also

pr_DB dist

Examples

data(iris)
xl<-iris[c(1:20,51:70,101:120),-5]
yl<-matrix(0,ncol=3,nrow=60)
yl[1:20,1]<-1
yl[21:40,2]<-1
yl[41:60,3]<-1
xu<-iris[-c(1:20,51:70,101:120),-5]
yu<-sslLLGC(xl,yl,xu)

Example output

SSL documentation built on May 29, 2017, 7:14 p.m.