SSL (version 0.1)

sslMarkovRandomWalks: t-step Markov Random Walks

Description

t-step Markov Random Walks

Usage

sslMarkovRandomWalks(xl, yl, xu, t = 10, dist.type = "Euclidean", k = 10, gamma = 1, improvement = 1e-04)

Arguments

xl

a n * p matrix or data.frame of labeled data.

yl

a n * 1 binary labels(1 or -1).

xu

a m * p matrix or data.frame of unlabeled data.

t

step size.

dist.type

character string; this parameter controls the type of distance measurement.(see dist or pr_DB).

k

an integer parameter controls a k-nearest neighbor graph.

gamma

a numeric parameter in the affinity matrix.

improvement

numeric. Maximum allowed distance between computed parameters in two successive iterations at the steady state.

Value

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

Details

sslMarkovRandomWalks transmits known labels to unlabeled data by t-step Markov random walks.Parameters are estimated by an EM algorithm.

References

Szummer, M., & Jaakkola, T. (2001). Partially labeled classification with M random walks. Advances in Neural Information Processing Systems, 14.

See Also

pr_DB dist

Examples

data(iris)
xl<-iris[c(1:20,51:70),-5]
xu<-iris[c(21:50,71:100),-5]
yl<-rep(c(1,-1),each=20)
yu<-sslMarkovRandomWalks(xl,yl,xu)