Principal component analysis (PCA) can be considered as ‘‘the mother of all methods in multivariate data analysis.’’ The aim of PCA is dimension reduction and PCA is the most frequently applied method for computing linear latent variables (components). PCA can be seen as a method to compute a new coordinate system formed by the latent variables, which is orthogonal, and where only the most informative dimensions are used. Latent variables from PCA optimally represent the distances between the objects in the high-dimensional variable space-remember, the distance of objects is considered as an inverse similarity of the objects. PCA considers all variables and accommodates the total data structure; it is a method for exploratory data analysis (unsupervised learning) and can be applied to practical any X-matrix; no y-data (properties) are considered and therefore not necessary.