Multiple Correspondence Analysis via Polynomial transformations of ordered categorical variables.

We present an alternative approach to Multiple Correspondence Analysis (MCA) that is appropriate when the data consist of ordered categorical variables. MCA displays objects (individuals, units) and variables as individual points and sets of category points in a low-dimensional space. We propose a hybrid decomposition on the basis of the classical indicator super-matrix, using the singular value decomposition, and the bivariate moment decomposition by orthogonal polynomials