  High-dimensional complex systems can be studied through multivariate
analysis, as Principal Component Analysis, however large samples of
observations frequently are needed for it. Here it is examined a method for
small samples based on clustering variables around latent variables (CLV) to
subject classification in two presumed groups. For it, a predictive model was
developed to generate datasets with two groups of cases whose variables show
randomness features (up to 30% of variables manifest difference between groups,
and up to 7% of those are correlated between them). The method recovered the
information of the latent factors to classify the subjects with 80 to 95% of
agreement, with positive relationship between the classifier precision and the
rate [number of variables / number of subjects].
