Multidimensional scaling methods for many-object sets: A review

Given a set of dissimilarities data between 11 objects, multidimensional sc aling is the problem of reconstructing a geometrical pattern of these objec ts, using n points, so that between-points distance corresponds to between- objects dissimilarity. Often, the collection of input data requires rating the dissimilarities between all 11(n - 1)/2 possible pairs of stimuli. When the number of stimuli is large, say n greater than or equal to 30, the num ber of pairs to be compared becomes very large and the similarity task inef ficient. Hence a question of major importance is how to increase the effici ency of the similarity task while maintaining satisfactory scaling solution s. This article reviews the main similarity task methods suitable for a lar ge objects set.