EMNearestMeanClassifier {RSSL} | R Documentation |
Semi-Supervised Nearest Mean Classifier using Expectation Maximization
Description
Expectation Maximization applied to the nearest mean classifier assuming Gaussian classes with a spherical covariance matrix.
Usage
EMNearestMeanClassifier(X, y, X_u, method = "EM", scale = FALSE,
eps = 1e-04)
Arguments
X |
matrix; Design matrix for labeled data |
y |
factor or integer vector; Label vector |
X_u |
matrix; Design matrix for unlabeled data |
method |
character; Currently only "EM" |
scale |
Should the features be normalized? (default: FALSE) |
eps |
Stopping criterion for the maximinimization |
Details
Starting from the supervised solution, uses the Expectation Maximization algorithm (see Dempster et al. (1977)) to iteratively update the means and shared covariance of the classes (Maximization step) and updates the responsibilities for the unlabeled objects (Expectation step).
References
Dempster, A., Laird, N. & Rubin, D., 1977. Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society. Series B, 39(1), pp.1-38.