Dimensionality Reduction: Simplify Your Data Complexity
Definition and Fundamental Purpose Principal Component Analysis (PCA) stands as one of the most widely utilized and foundational statistical techniques in the field of multivariate data analysis. At its core, PCA is a robust method designed to reduce the dimensionality of complex, high-dimensional datasets while ensuring that the maximum amount of original information—specifically variance—is retained. […]
Procrustes Rotation: Finding Order in Complex Data
Introduction and Core Definition Procrustes rotation is a fundamental technique within multivariate statistics, particularly prominent in psychometrics and factor analysis. It is defined as a linear transformation applied to the points in a data matrix (Matrix A) in order to achieve the maximum possible congruence with the points defined in a second, predefined target matrix […]