Effect on Classification Error of Random Permutations of Features in Representing Multivariate Data by Faces

Abstract A graphical method of representing multivariate data consists of drawing a cartoon of a face determined by 18 parameters. A sample of vector observations of dimension d ≤ 18 is converted to faces by assigning components of the vector to facial parameters. We report an experiment which estimates that the effect of a random permutation in the assignment of parameters may affect the error rate in a classification task using these faces by a factor of about 25 percent.