Principal Component Analysis (PCA) is a statistical technique used to simplify complex data sets. When discussing this term, it can be helpful to familiarize oneself with some synonyms that have similar meanings and applications. One such synonym is dimensionality reduction, which refers to the process of reducing the number of variables in a dataset without losing too much information. Another synonym is factor analysis, which is a method used to explore relationships among a large number of variables by reducing them into a smaller set of unobservable factors. Lastly, eigenvalue decomposition can be used as a synonym because it involves the decomposition of a matrix into a product of eigenvectors and eigenvalues, which is a key step in PCA.