What is Preference Mapping

As the capacity to summarize of the preference map diminishes with the number of consumers (the number of axes to interpret increases), a non-metric PCA is sometimes used to reduce the number of necessary axes. The non-metric PCA consists of a monotone transform of the data so that the variability explained by the k (k=2 or 3) first axes is maximized. This transform implies that we consider the ratings have an ordinal meaning, and that the distances or ratios between the ratings are not important. To reduce the number of axes you might also want to group the consumers and to perform the PCA with the groups as variables.