A New Computational Method to Fit the Weighted Euclidean Distance Model

This paper describes a computational method for weighted euclidean distance scaling which combines aspects of an “analytic” solution with an approach using loss functions. We justify this new method by giving a simplified treatment of the algebraic properties of a transformed version of the weighted distance model. The new algorithm is much faster than INDSCAL yet less arbitrary than other “analytic” procedures. The procedure, which we call SUMSCAL ( su bjective m etric scal ing), gives essentially the same solutions as INDSCAL for two moderate-size data sets tested.