% Generated by roxygen2: do not edit by hand

% Please edit documentation in R/S4VM.R

\name{S4VM}

\alias{S4VM}

\title{Safe Semi-supervised Support Vector Machine (S4VM)}

\usage{

S4VM(X, y, X_u = NULL, C1 = 100, C2 = 0.1, sample_time = 100,

gamma = 0, x_center = FALSE, scale = FALSE, lambda_tradeoff = 3)

}

\arguments{

\item{X}{matrix; Design matrix for labeled data}

\item{y}{factor or integer vector; Label vector}

\item{X_u}{matrix; Design matrix for unlabeled data}

\item{C1}{double; Regularization parameter for labeled data}

\item{C2}{double; Regularization parameter for unlabeled data}

\item{sample_time}{integer; Number of low-density separators that are generated}

\item{gamma}{double; Width of RBF kernel}

\item{x_center}{logical; Should the features be centered?}

\item{scale}{logical; Should the features be normalized? (default: FALSE)}

\item{lambda_tradeoff}{numeric; Parameter that determines the amount of "risk" in obtaining a worse solution than the supervised solution, see Li & Zhou (2011)}

}

\value{

S4VM object with slots:

\item{predictions}{Predictions on the unlabeled objects}

\item{labelings}{Labelings for the different clusters}

}

\description{

R port of the MATLAB implementation of Li & Zhou (2011) of the Safe Semi-supervised Support Vector Machine.

}

\details{

The method randomly generates multiple low-density separators (controlled by the sample_time parameter) and merges their predictions by solving a linear programming problem meant to penalize the cost of decreasing the performance of the classifier, compared to the supervised SVM. S4VM is a bit of a misnomer, since it is a transductive method that only returns predicted labels for the unlabeled objects. The main difference in this implementation compared to the original implementation is the clustering of the low-density separators: in our implementation empty clusters are not dropped during the k-means procedure. In the paper by Li (2011) the features are first normalized to [0,1], which is not automatically done by this function. Note that the solution may not correspond to a linear classifier even if the linear kernel is used.

}

\examples{

library(RSSL)

library(dplyr)

library(ggplot2)

library(tidyr)

set.seed(1)

df_orig <- generateSlicedCookie(100,expected=TRUE)

df <- df_orig \%>\% add_missinglabels_mar(Class~.,0.95)

g_s <- SVM(Class~.,df,C=1,scale=TRUE,x_center=TRUE)

g_s4 <- S4VM(Class~.,df,C1=1,C2=0.1,lambda_tradeoff = 3,scale=TRUE,x_center=TRUE)

labs <- g_s4@labelings[-c(1:5),]

colnames(labs) <- paste("Class",seq_len(ncol(g_s4@labelings)),sep="-")

df \%>\%

filter(is.na(Class)) \%>\%

bind_cols(data.frame(labs,check.names = FALSE)) \%>\%

select(-Class) \%>\%

gather(Classifier,Label,-X1,-X2) \%>\%

ggplot(aes(x=X1,y=X2,color=Label)) +

geom_point() +

facet_wrap(~Classifier,ncol=5)

df \%>\%

filter(is.na(Class)) \%>\%

mutate(prediction=g_s4@predictions) \%>\%

ggplot(aes(x=X1,y=X2,color=prediction)) +

geom_point() +

stat_classifier(color="black", classifiers=list(g_s))

}

\references{

Yu-Feng Li and Zhi-Hua Zhou. Towards Making Unlabeled Data Never Hurt. In: Proceedings of the 28th International Conference on Machine Learning (ICML'11), Bellevue, Washington, 2011.