Updated Second Moment Least Squares Classifier
This methods uses the closed form solution of the supervised least squares problem, except that the second moment matrix (X'X) is exchanged with a second moment matrix that is estimated based on all data. See for instance Shaffer1991, where in this implementation we use all data to estimate E(X'X), instead of just the labeled data. This method seems to work best when the data is first centered x_center=TRUE and the outputs are scaled using y_scale=TRUE.
USMLeastSquaresClassifier(X, y, X_u, lambda = 0, intercept = TRUE, x_center = FALSE, scale = FALSE, y_scale = FALSE, ..., use_Xu_for_scaling = TRUE)
X |
matrix; Design matrix for labeled data |
y |
factor or integer vector; Label vector |
X_u |
matrix; Design matrix for unlabeled data |
lambda |
numeric; L2 regularization parameter |
intercept |
logical; Whether an intercept should be included |
x_center |
logical; Should the features be centered? |
scale |
logical; Should the features be normalized? (default: FALSE) |
y_scale |
logical; whether the target vector should be centered |
... |
Not used |
use_Xu_for_scaling |
logical; whether the unlabeled objects should be used to determine the mean and scaling for the normalization |
Shaffer, J.P., 1991. The Gauss-Markov Theorem and Random Regressors. The American Statistician, 45(4), pp.269-273.
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,
WellSVM,
svmlin()
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