WellSVM: WellSVM for Semi-supervised Learning

In RSSL: Implementations of Semi-Supervised Learning Approaches for Classification

WellSVM for Semi-supervised Learning

Description

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.

Usage

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)

Arguments

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

References

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.

See Also

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()

Examples

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)

RSSL documentation built on March 31, 2023, 7:27 p.m.