Skip to main content
Log in

Two level minimization in multidimensional scaling

  • Original Article
  • Published:
Journal of Global Optimization Aims and scope Submit manuscript

Abstract

Multidimensional scaling with city block norm in embedding space is considered. Construction of the corresponding algorithm is reduced to minimization of a piecewise quadratic function. The two level algorithm is developed combining combinatorial minimization at upper level with local minimization at lower level. Results of experimental investigation of the efficiency of the proposed algorithm are presented as well as examples of its application to visualization of multidimensional data.

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

  • An L., Tao P. (2001) D.C. programing approach to the multidimensional scaling problem. In: Pardalos P., Varbrand P. (eds) From Local to Global Optimization. Kluwer, Dodrecht, pp. 231–276

    Google Scholar 

  • Borg I., Groenen P. (1997) Modern Multidimensional Scaling. Springer, New York

    Google Scholar 

  • Brusco M.J. (2001) A simulated annealing heuristics for unidimensional and multidimensional (city block) scaling of symmetric proximity matrices. J. Classif. 18, 3–33

    Google Scholar 

  • Corne D., Dorigo M., Glover F. (eds) (1999) New Ideas in Optimization. McGraw-Hill, Maidenhead, England

    Google Scholar 

  • Cox T., Cox M. (2001) Multidimensional Scaling. Chapman and Hall/CRC, Boca Raton

    Google Scholar 

  • De Leeuw J. (1984) Differentiability of Kruskal’s stress at a local minimum. Psychometrika 149, 111–113

    Article  Google Scholar 

  • De Leeuw J., Heiser W. (1982) Theory of multidimentional scaling. In: Krishnaiah P.R. (eds) Handbook of Statistics, vol. 2. North Holland, Amsterdam, pp. 285–316

    Google Scholar 

  • Everett J. (2001) Algorithms for multidimensional scaling. In: Chambers L. (eds) The practical handbook of genetic algorithms. Chapman and Hall/CRC, Boca Raton, pp. 2003–2233

    Google Scholar 

  • Festa P., Pardalos P.M., Resende M.G.C., Ribeiro C.C. (2002) Randomized heuristics for the max-cut problem. Optim. Methods Softw. 7, 1033–1058

    Article  Google Scholar 

  • Groenen, P.: The Majorization Approach to multidimentional scaling, p. 110. DSWO, Amsterdam (1993)

  • Groenen P., Mathar R., Heiser W. (1995) The majorization approach to multidimensional scaling for minkowski distances. J. Classif. 12, 3–19

    Article  Google Scholar 

  • Groenen, P., Mathar, R., Trejos, J.: Global optimization methods for MDS applied to mobile communications. In: Gaul, W., Opitz, O., Schander, M. (eds.) Data Analysis: Scientific Models and Practical Applications, pp. 459–475. Springer, (2000)

  • Horst R., Pardalos P., Thoai N. (1995) Introduction to global optimization. Kluwer, Dodrecht

    Google Scholar 

  • Klock H., Buhmann J. (1999) Data visualization by multidimensional scaling: a deterministic annealing approach. Pattern Recogn. 33(4): 651–669

    Article  Google Scholar 

  • Leng P.L., Lau K. (2004) Estimating the city-block two-dimensional scaling model with simulated annealing. Eur. J. Oper. Res. 158, 518–524

    Article  Google Scholar 

  • Mathar R. (1997) Multidimensionale Skalierung. Teubner, Stuttgart

    Google Scholar 

  • Mathar R. (1996) A hybrid global optimization algorithm for multidimensional scaling. In: Klar R., Opitz O. (eds) Classification and knowledge organization. Springer, Berlin, 63–71

    Google Scholar 

  • Mathar R., Žilinskas A. (1993) On global optimization in two-dimensional scaling. Acta Appl. Math. 33, 109–118

    Article  Google Scholar 

  • Michalewicz Z. (1996) Genetic Algorithms + Data Structures = Evolution Programs. Springer, Berlin

    Google Scholar 

  • Press W. et al. (2002) Numerical Recipes in C++. Cambridge University Press, Cambridge

    Google Scholar 

  • Törn, A., Žilinskas, A.: Global Optimization, Lecture Notes in Computer Science, vol. 350, pp. 1–250 (1989)

  • Žilinskas A. (2003) On the distribution of the distance between two points in a cube. Random Oper. Stoch. Eqs. 11, 21–24

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Antanas Žilinskas.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Žilinskas, A., Žilinskas, J. Two level minimization in multidimensional scaling. J Glob Optim 38, 581–596 (2007). https://doi.org/10.1007/s10898-006-9097-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10898-006-9097-x

Keywords

Navigation