Implicitly Constrained Semi-supervised Linear Discriminant Classifier
Semi-supervised version of Linear Discriminant Analysis using implicit constraints as described in (Krijthe & Loog 2014). This method finds the soft labeling of the unlabeled objects, whose resulting LDA solution gives the highest log-likelihood when evaluated on the labeled objects only. See also ICLeastSquaresClassifier.
ICLinearDiscriminantClassifier(X, y, X_u, prior = NULL, scale = FALSE, init = NULL, sup_prior = FALSE, x_center = FALSE, ...)
X |
design matrix of the labeled objects |
y |
vector with labels |
X_u |
design matrix of the labeled objects |
prior |
set a fixed class prior |
scale |
logical; Should the features be normalized? (default: FALSE) |
init |
not currently used |
sup_prior |
logical; use the prior estimates based only on the labeled data, not the imputed labels (default: FALSE) |
x_center |
logical; Whether the data should be centered |
... |
Additional Parameters, Not used |
Krijthe, J.H. & Loog, M., 2014. Implicitly Constrained Semi-Supervised Linear Discriminant Analysis. In International Conference on Pattern Recognition. Stockholm, pp. 3762-3767.
Other RSSL classifiers:
EMLeastSquaresClassifier,
EMLinearDiscriminantClassifier,
GRFClassifier,
ICLeastSquaresClassifier,
KernelLeastSquaresClassifier,
LaplacianKernelLeastSquaresClassifier(),
LaplacianSVM,
LeastSquaresClassifier,
LinearDiscriminantClassifier,
LinearSVM,
LinearTSVM(),
LogisticLossClassifier,
LogisticRegression,
MCLinearDiscriminantClassifier,
MCNearestMeanClassifier,
MCPLDA,
MajorityClassClassifier,
NearestMeanClassifier,
QuadraticDiscriminantClassifier,
S4VM,
SVM,
SelfLearning,
TSVM,
USMLeastSquaresClassifier,
WellSVM,
svmlin()
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