Spherical dissimilarities and normed multidimensional positioning

Our concern here, is the characterization of dissimilarity indexes defined over finite sets, whose spatial representation is spherical. Consequently, we propose a methodology (Normed MultiDimensional Scaling) to determine the spherical euclidean representation of a set of items best accounting for t he initial dissimilarity between items. This methodology has the advantage of being graphically readable on individual qualities of projection like th e normed PCA, of which it constitutes a generalization. Moreover, it avoids the arbitrary character of spherical encoding which the rise of similitude functions currently used in MDS, implies.