Minimum Entropy Criterion for Analytic Rotation

Abstract

Minimum entropy is described as an analytic criterion for rotation to simple structure for both principal component and factor analysis data matrices. Minimum entropy rotated matrices come closer to achieving the ideal simple structure than is possible using the varimax method in the sense that a greater proportion of absolute values of the coefficients in the rotated matrix lie closer to zero. This allows greater ease of recognition of the underlying structure in the original data array. The concept of rotation is extended to include rotation of principal components. Numerical examples are given to illustrate the application of minimum entropy rotation in both principal component analysis and factor analysis.