A Metric Multidimensional Scaling
نویسندگان
چکیده
| 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 2-dimensional Euclidean space. On the one hand, this paper proposes a generalization of this approach to greater dimensions. Thus it is found that by this method, d(N ?2)+1 is the theoretical minimum number of appropriate exact distances for the unique representation of N points in a d-dimensional Eu-clidean space. On the other hand, the method is evaluated by a Monte Carlo study on the basis of basic parameters.
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