Principal component analysis (PCA) is an important tool for dimension reduction in multivariate analysis. Regularized PCA methods, such as sparse PCA and functional PCA, have been developed to ...

R software will be used in this course. This course covers: Differences between multivariate analysis and univariate analysis Differences between dimension reduction and clustering Principle Component ...

A common objective in exploratory multivariate analysis is to identify a subset of the variables which conveys the main features of the whole sample. Analysis of a well-known multivariate data set ...