title: Multidimensional Scaling Methods for Many-Object Sets: A Review.
abstract: Given a set of dissimilarities data between n objects, multidimensional scaling is the problem of reconstructing a geometrical pattern of these objects, 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 n(n - 1)/2 possible pairs of stimuli. When the number of stimuli is large, say n $ 30, the number of pairs to be compared becomes very large and the similarity task inefficient. Hence a question of major importance is how to increase the efficiency of the similarity task while maintaining satisfactory scaling solutions. This article reviews the main similarity task methods suitable for a large objects set.
authors.forename: L
authors.initials: L
authors.lastname: Tsogo
authors.forename: M H
authors.initials: MH
authors.lastname: Masson
authors.forename: A
authors.initials: A
authors.lastname: Bardot