Skip to main content
Log in

The majorization approach to multidimensional scaling for Minkowski distances

  • Published:
Journal of Classification Aims and scope Submit manuscript

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  • ARABIE, P. (1991), “Was Euclid an Unnecessarily Sophisticated Psychologist?”Psychometrika, 56, 567–587.

    Google Scholar 

  • CARROLL, J. D., and WISH, M. (1977), “Multidimensional Perceptual Models and Measurement Methods,” inHandbook of Perception, Vol. II Eds., E. C. Carterette and M.P. Friedman, New York: Academic Press, 391–447.

    Google Scholar 

  • DE LEEUW, J. (1977), “Applications of Convex Analysis to Multidimensional Scaling,” inRecent Developments in Statistics, Eds., J. R. Barra, F. Brodeau, G. Romier and B. van Cutsem, Amsterdam: North-Holland, 133–145.

    Google Scholar 

  • DE LEEUW, J. (1984), “Differentiability of Kruskal's Stress at a Local Minimum,”Psychometrika, 49, 111–113.

    Google Scholar 

  • DE LEEUW, J. (1988), “Convergence of the Majorization Method for Multidimensional Scaling,”Journal of Classification, 5, 163–180.

    Google Scholar 

  • DE LEEUW, J. (1992),Fitting Distances by Least Squares, unpublished manuscript, Los Angeles: UCLA.

    Google Scholar 

  • DE LEEUW, J., and HEISER, W. J. (1977), “Convergence of Correction-Matrix Algorithms for Multidimensional Scaling” inGeometric Representations of Relational Data, Ed., J. C. Lingoes, Ann Arbor, Michigan: Mathesis Press, 735–751.

    Google Scholar 

  • DE LEEUW, J., and HEISER, W. J. (1980), “Multidimensional Scaling with Restrictions on the Configuration,” inMultivariate Analysis V, Ed., P. R. Krishnaiah, Amsterdam: North-Holland, 501–522.

    Google Scholar 

  • GREEN, P. E., CARMONE, F. J. Jr, and SMITH, S. M. (1989),Multidimensional Scaling, Concepts and Applications, Boston: Allyn and Bacon.

    Google Scholar 

  • GROENEN, P. J. F., and HEISER, W. J. (1991),An Improved Tunneling Function for Finding a Decreasing Series of Local Minima, internal report RR-91-06, Leiden: Department of Data Theory.

    Google Scholar 

  • GUTTMAN, L. (1968), “A General Nonmetric Technique for Finding the Smallest Coordinate Space for a Configuration of Points,”Psychometrika, 33, 469–506.

    Google Scholar 

  • HEISER, W. J. (1988), “Multidimensional Scaling with Least Absolute Residuals,” inClassification and Related Methods of Data Analysis, Ed., H. H. Bock, Amsterdam: North-Holland, 455–462.

    Google Scholar 

  • HEISER, W. J. (1989), “The City-Block Model for Three-Way Multidimensional Scaling,” inMultiway Data Analysis, Eds., R. Coppi and S. Bolasco, Amsterdam: North-Holland, 395–404.

    Google Scholar 

  • HEISER, W. J. (1991), “A Generalized Majorization Method for Least Squares Multidimensional Scaling of Pseudodistances That may be Negative,”Psychometrika, 56, 7–27.

    Google Scholar 

  • HUBERT, L. J., and ARABIE, P. (1986), “Unidimensional Scaling and Combinatorial Optimization,” inMultidimensional Data Analysis, Eds., J. De Leeuw, W. J. Heiser, J. Meulman and F. Critchley, Leiden: DSWO Press, 181–196.

    Google Scholar 

  • HUBERT, L. J., ARABIE, P., and HESSON-MCINNIS, M. (1992), ”Multidimensional Scaling in the City-Block Metric: A Combinatorial Approach,”Journal of Classification, 9, 211–236.

    Google Scholar 

  • KRUSKAL, J. B. (1964a), “Multidimensional Scaling by Optimizing Goodness of Fit to a Non-Metric Hypothesis,”Psychometrika, 29, 1–27.

    Google Scholar 

  • KRUSKAL, J. B. (1964b), “Nonmetric Multidimensional Scaling: A Numerical Method,”Psychometrika, 29, 115–129.

    Google Scholar 

  • KRUSKAL, J. B. (1977), “Multidimensional Scaling and Other Methods for Discovering Structure,” inStatistical Methods for Digital Computers, Vol III, Eds., K. Enslein, A. Ralston and H. S. Wilf, New York: Wiley, 296–339.

    Google Scholar 

  • KRUSKAL, J. B., YOUNG, F. W., and SEERY, J. B. (1977),How to Use KYST-2, a Very Flexible Program to do Multidimensional Scaling and Unfolding, Murray Hill, NJ: AT&T Bell Labs.

    Google Scholar 

  • MATHAR, R., and GROENEN, P. J. F. (1991), “Algorithms in Convex Analysis Applied to Multidimensional Scaling,” inSymbolic-Numeric Data Analysis and Learning, Eds., E. Diday, and Y. Lechevallier, Commack, New York: Nova Science, 45–56.

    Google Scholar 

  • MATHAR, R., and MEYER, R. (1992),Algorithms in Convex Analysis to Fit l p-distance matrices, unpublished report, Aachen: RWTH.

    Google Scholar 

  • MEULMAN, J. J. (1986),A Distance Approach to Nonlinear Multivariate Analysis, Leiden: DSWO Press.

    Google Scholar 

  • PETERS, G., and WILKINSON, J. H. (1971), “The Calculation of Specified Eigenvectors by Inverse Iteration,” inHandbook for Automatic Computation, Vol. II, Linear Algebra, Eds., J. H. Wilkinson and C. Reinsch, Berlin: Springer, 418–439.

    Google Scholar 

  • ZANGWILL, W. I. (1969),Nonlinear Programming, a Unified Approach, Englewood Cliffs, NJ: Prentice-Hall.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Additional information

Acknowledgment: We would like to thank three anonymous referees for carefully reading the manuscript and for several valuable remarks that have improved this paper. A previous version of this paper has appeared as chapter 7 in Groenen, P.J.F.,The Majorization Approach to Multidimensional Scaling: Some Problems and Extensions, DSWO Press, Leiden, 1993.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Groenen, P.J.F., Mathar, R. & Heiser, W.J. The majorization approach to multidimensional scaling for Minkowski distances. Journal of Classification 12, 3–19 (1995). https://doi.org/10.1007/BF01202265

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1007/BF01202265

Key Words

Navigation