Linear discriminant analysis

Summary

Linear discriminant analysis assigns an observation to one of several classes with a rule whose decision boundaries are hyperplanes. It is the optimal rule when each class has a multivariate normal distribution and all classes share one covariance matrix. In practice the means and the common covariance are estimated from labelled data. It is closely tied to Fisher's linear discriminant, which also gives a dimension reduction.