ABSTRACT
One of the problems that arises when dealing with a large data set is that its dimensionality, that is, the number of variables, can be very large. This can make the analysis difficult. A common approach to address this issue is to first reduce the dimensionality to a smaller set of variables and then apply the appropriate algorithm. Two classical approaches for reducing the dimensionality of a data set are presented, namely, principal component analysis (PCA), and linear discriminant analysis (LDA) and its generalization to multiple discriminant analysis (MDA). A set of exercises is given at the end of the Chapter.
