Description Usage Arguments Value Author(s) References See Also Examples
sslRegress
develops a regularization framework on graphs.It supports many
kinds of distance measurements and graph representations. However, it only supports binary classifications.
1 2 | sslRegress(xl, yl, xu, graph.type = "exp", dist.type = "Euclidean", alpha,
alpha1, alpha2, p = 2, method = "Tikhonov", gamma = 1)
|
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. |
graph.type |
character string; which type of graph should be created? Options
include
|
dist.type |
character string; this parameter controls the type of distance measurement.(see |
alpha |
numeric parameter needed when |
alpha1 |
numeric parameter needed when |
alpha2 |
numeric parameter needed when |
p |
an ineger parameter controls the power of Laplacian for regularization. |
method |
character string; this parameter choose two possible algorithms:"Tikhonov" means Tikhonov regularization;"Interpolated" means Interpolated regularization. |
gamma |
a parameter of Tikhonov regularization. |
a m * 1 integer vector representing the predicted labels of unlabeled data(1 or -1).
Junxiang Wang
Belkin, M., Matveeva, I., & Niyogi, P. (2004a). Regularization and semisupervised learning on large graphs. COLT
1 2 3 4 5 6 7 8 9 10 | 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)
# Tikhonov regularization
yu1<-sslRegress(xl,yl,xu,graph.type="tanh",alpha1=-2,alpha2=1)
yu2<-sslRegress(xl,yl,xu,graph.type="exp",alpha = 1)
# Interpolated regularization
yu3<-sslRegress(xl,yl,xu,graph.type="tanh",alpha1=-2,alpha2=1,method="Interpolated")
yu4<-sslRegress(xl,yl,xu,graph.type="exp",alpha = 1,method="Interpolated")
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