Principal components

Principal components analysis (PCA) is a statistical technique that allows to identify underlying linear patterns in a data set so it can be expressed in terms of other data set of significatively lower dimension without much loss of information. The final data set should be able to explain most of the variance of the original data set by making a variable reduction. The final variables will be named as principal components. The following image depicts the activity diagram that shows each step of the principal components analysis that will be explained in detail later. In order to illustrate the process described in the previous diagram, we are going to make use of the following data set which has two dimensions.