SELECTING THE OPTIMAL MULTIDIMENSIONAL SCALING PROCEDURE FOR METRIC DATA WITH R ENVIRONMENT

A Metric Multidimensional Scaling

| Multidimensional Scaling (MDS) techniques always pose the problem of analysing a large number N of points, without collecting all N(N?1) 2 possible interstimuli dissimilarities, and while keeping satisfactory solutions. In the case of metric MDS, it was found that a theoretical minimum of appropriate 2N ?3 exact Euclidean distances are suf-cient for the unique representation of N points in a ...

Reliability and Metric Multidimensional Scaling

Generalized Non-metric Multidimensional Scaling

We consider the non-metric multidimensional scaling problem: given a set of dissimilarities ∆, find an embedding whose inter-point Euclidean distances have the same ordering as ∆. In this paper, we look at a generalization of this problem in which only a set of order relations of the form dij < dkl are provided. Unlike the original problem, these order relations can be contradictory and need no...

Better initial configurations for metric multidimensional scaling

Multidimensional scaling (MDS) is a collection of data analytic techniques for constructing con$gurations of points from dissimilarity information about interpoint distances. Two popular measures of how well the constructed distances $t the observed dissimilarities are the raw stress and sstress criteria, each of which must be minimized by numerical optimization. Because iterative procedures fo...

Using Optimal Classification for multidimensional scaling analysis of linguistic data

Multidimensional scaling (MDS) is a technique for visualizing the relationships among data that are similar to each other on very many dimensions. For example, meanings of words such as indefinite pronouns are similar to each other by virtue of being expressed by the same indefinite pronoun in one language or another (Haspelmath 1997). If one compares indefinite pronouns of a large number of la...