WellSVM for Semi-superivsed Learning
WellSVM is a minimax relaxation of the mixed integer programming problem of finding the optimal labels for the unlabeled data in the SVM objective function. This implementation is a translation of the Matlab implementation of Li (2013) into R.
WellSVM(X, y, X_u, C1 = 1, C2 = 0.1, gamma = 1, x_center = TRUE, scale = FALSE, use_Xu_for_scaling = FALSE, max_iter = 20)
X |
matrix; Design matrix for labeled data |
y |
factor or integer vector; Label vector |
X_u |
matrix; Design matrix for unlabeled data |
C1 |
double; A regularization parameter for labeled data, default 1; |
C2 |
double; A regularization parameter for unlabeled data, default 0.1; |
gamma |
double; Gaussian kernel parameter, i.e., k(x,y) = exp(-gamma^2||x-y||^2/avg) where avg is the average distance among instances; when gamma = 0, linear kernel is used. default gamma = 1; |
x_center |
logical; Should the features be centered? |
scale |
logical; Should the features be normalized? (default: FALSE) |
use_Xu_for_scaling |
logical; whether the unlabeled objects should be used to determine the mean and scaling for the normalization |
max_iter |
integer; Maximum number of iterations |
Y.-F. Li, I. W. Tsang, J. T. Kwok, and Z.-H. Zhou. Scalable and Convex Weakly Labeled SVMs. Journal of Machine Learning Research, 2013.
R.-E. Fan, P.-H. Chen, and C.-J. Lin. Working set selection using second order information for training SVM. Journal of Machine Learning Research 6, 1889-1918, 2005.
Other RSSL classifiers:
EMLeastSquaresClassifier,
EMLinearDiscriminantClassifier,
GRFClassifier,
ICLeastSquaresClassifier,
ICLinearDiscriminantClassifier,
KernelLeastSquaresClassifier,
LaplacianKernelLeastSquaresClassifier(),
LaplacianSVM,
LeastSquaresClassifier,
LinearDiscriminantClassifier,
LinearSVM,
LinearTSVM(),
LogisticLossClassifier,
LogisticRegression,
MCLinearDiscriminantClassifier,
MCNearestMeanClassifier,
MCPLDA,
MajorityClassClassifier,
NearestMeanClassifier,
QuadraticDiscriminantClassifier,
S4VM,
SVM,
SelfLearning,
TSVM,
USMLeastSquaresClassifier,
svmlin()
library(RSSL)
library(ggplot2)
library(dplyr)
set.seed(1)
df_orig <- generateSlicedCookie(200,expected=TRUE)
df <- df_orig %>%
add_missinglabels_mar(Class~.,0.98)
classifiers <- list("Well"=WellSVM(Class~.,df,C1 = 1, C2=0.1,
gamma = 0,x_center=TRUE,scale=TRUE),
"Sup"=SVM(Class~.,df,C=1,x_center=TRUE,scale=TRUE))
df %>%
ggplot(aes(x=X1,y=X2,color=Class)) +
geom_point() +
coord_equal() +
stat_classifier(aes(color=..classifier..),
classifiers = classifiers)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.