what is PCA

Principal Component Analysis (PCA) is a dimensionality reduction technique that transforms a dataset into a set of orthogonal components, ordered by the amount of variance they capture, to simplify data analysis while preserving as much information as possible.

eigenvalue

% of variability explained by principal component

Principal Component

A vector that describes deviation from a mean level. Principal components are orthogonal to each other. The complete set of principal components explains all of variability in data set.