sslMarkovRandomWalks: t-step Markov Random Walks

In SSL: Semi-Supervised Learning

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.

Details

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

Value

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

Author(s)

Junxiang Wang

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)

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