Exploring Multivariate Data Using the Minor Principal Components

The minor principal components of a random vector x are the orthonormal linear combinations of the variables in x which have minimum variance. This fact may be used to detect interrelations amongst the variables as well as to identify those variables which can be predicted with high accuracy from the others. Application of these ideas to the variable selection problem in multiple regression and to the identification of outliers in multivariate data are discussed.