Abstract
The aim of this paper is to present basic principles of common multivariate statistical approaches to dimensionality reduction and to discuss three particular approaches, namely feature extraction, (prior) variable selection, and sparse variable selection. Their important examples are also presented in the paper, which includes the principal component analysis, minimum redundancy maximum relevance variable selection, and nearest shrunken centroid classifier with an intrinsic variable selection.
Each of the three methods is illustrated on a real dataset with a biomedical motivation, including a biometric identification based on keystroke dynamics or a study of metabolomic profiles. Advantages and benefits of performing dimensionality reduction of multivariate data are discussed.