In almost all fields of applications, statisticians are confronted with highly complex data sets with large dimensions. Dimension reduction methods naturally provide a better understanding of the data and reveal hidden structures. The book provides tools with a unifying statistical theory to recover hidden structures, latent variables, or latent subspaces in multivariate and dependent data. Throughout the book, the theory is illustrated with examples on practical data sets.