The majorization approach to multidimensional scaling for Minkowski distances

The majorization method for multidimensional scaling with Kruskal's STRESS has been limited to Euclidean distances only. Here we extend the majorization algorithm to deal with Minkowski distances with 1≤p≤2 and suggest an algorithm that is partially based on majorization forp outside this range. We give some convergence proofs and extend the zero distance theorem of De Leeuw (1984) to Minkowski distances withp>1.

[1]  J. Kruskal Nonmetric multidimensional scaling: A numerical method , 1964 .

[2]  J. Kruskal Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis , 1964 .

[3]  L. Guttman A general nonmetric technique for finding the smallest coordinate space for a configuration of points , 1968 .

[4]  E. M. L. Beale,et al.  Nonlinear Programming: A Unified Approach. , 1970 .

[5]  J. H. Wilkinson,et al.  The Calculation of Specified Eigenvectors by Inverse Iteration , 1971 .

[6]  J. Carroll,et al.  Chapter 12 – MULTIDIMENSIONAL PERCEPTUAL MODELS AND MEASUREMENT METHODS , 1974 .

[7]  Jan de Leeuw,et al.  Differentiability of Kruskal's stress at a local minimum , 1984 .

[8]  J. Meulman A Distance Approach to Nonlinear Multivariate Analysis , 1986 .

[9]  J. Leeuw Convergence of the majorization method for multidimensional scaling , 1988 .

[10]  Paul E. Green,et al.  Multidimensional Scaling: Concepts and Applications , 1989 .

[11]  Phipps Arabie,et al.  Was euclid an unnecessarily sophisticated psychologist? , 1991 .

[12]  Rudolf Mathar,et al.  Algorithms in convex analysis applied to Multidimensional Scaling , 1991 .

[13]  W. Heiser A generalized majorization method for least souares multidimensional scaling of pseudodistances that may be negative , 1991 .

[14]  L. Hubert,et al.  Multidimensional scaling in the city-block metric: A combinatorial approach , 1992 .

[15]  R. Mathar,et al.  Algorithms in Convex Analysis to Fit lp-Distance Matrices , 1994 .