public class RDA
implements SoftClassifier<double>, java.io.Serializable
Regularized discriminant analysis. RDA is a compromise between LDA and QDA,
which allows one to shrink the separate covariances of QDA toward a common
variance as in LDA. This method is very similar in flavor to ridge regression.
The regularized covariance matrices of each class is
Σk(α) = α Σk + (1 - α) Σ.
The quadratic discriminant function is defined using the shrunken covariance
matrices Σk(α). The parameter α in [0, 1]
controls the complexity of the model. When α is one, RDA becomes QDA.
While α is zero, RDA is equivalent to LDA. Therefore, the
regularization factor α allows a continuum of models between LDA and QDA.
Predicts the class label of an instance and also calculate a posteriori
probabilities. Classifiers may NOT support this method since not all
classification algorithms are able to calculate such a posteriori