ABSTRACT
Principal components analysis (PCA) is a method for finding low-dimensional representations of a data set that retain as much of the original variation as possible. Dimension reduction methods, such as PCA, focus on reducing the feature space, allowing most of the information or variability in the data set to be explained using fewer features; in the case of PCA, these new features will also be uncorrelated. The sum of the eigenvalues is equal to the number of variables entered into the PCA; however, the eigenvalues will range from greater than one to near zero. There have been many robust variants of PCA that act to iteratively discard data points that are poorly described by the initial components. The rationale for using the eigenvalue criterion is that each component should explain at least one variable’s worth of the variability, and therefore, the eigenvalue criterion states that only components with eigenvalues greater than 1 should be retained.
