Moment Constrained Semi-supervised Linear Discriminant Analysis.
A linear discriminant classifier that updates the estimates of the means and covariance matrix based on unlabeled examples.
MCLinearDiscriminantClassifier(X, y, X_u, method = "invariant", prior = NULL, x_center = TRUE, scale = FALSE)
X |
matrix; Design matrix for labeled data |
y |
factor or integer vector; Label vector |
X_u |
matrix; Design matrix for unlabeled data |
method |
character; One of c("invariant","closedform") |
prior |
Matrix (k by 1); Class prior probabilities. If NULL, estimated from data |
x_center |
logical; Should the features be centered? |
scale |
logical; Should the features be normalized? (default: FALSE) |
This method uses the parameter updates of the estimated means and covariance proposed in (Loog 2014). Using the method="invariant" option, uses the scale invariant parameter update proposed in (Loog 2014), while method="closedform" using the non-scale invariant version from (Loog 2012).
Loog, M., 2012. Semi-supervised linear discriminant analysis using moment constraints. Partially Supervised Learning, LNCS, 7081, pp.32-41.
Loog, M., 2014. Semi-supervised linear discriminant analysis through moment-constraint parameter estimation. Pattern Recognition Letters, 37, pp.24-31.
Other RSSL classifiers:
EMLeastSquaresClassifier,
EMLinearDiscriminantClassifier,
GRFClassifier,
ICLeastSquaresClassifier,
ICLinearDiscriminantClassifier,
KernelLeastSquaresClassifier,
LaplacianKernelLeastSquaresClassifier(),
LaplacianSVM,
LeastSquaresClassifier,
LinearDiscriminantClassifier,
LinearSVM,
LinearTSVM(),
LogisticLossClassifier,
LogisticRegression,
MCNearestMeanClassifier,
MCPLDA,
MajorityClassClassifier,
NearestMeanClassifier,
QuadraticDiscriminantClassifier,
S4VM,
SVM,
SelfLearning,
TSVM,
USMLeastSquaresClassifier,
WellSVM,
svmlin()
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