Journal of Classification

, Volume 5, Issue 2, pp 163–180

Convergence of the majorization method for multidimensional scaling

  • Jan de Leeuw
Authors Of Articles

DOI: 10.1007/BF01897162

Cite this article as:
de Leeuw, J. Journal of Classification (1988) 5: 163. doi:10.1007/BF01897162

Abstract

In this paper we study the convergence properties of an important class of multidimensional scaling algorithms. We unify and extend earlier qualitative results on convergence, which tell us when the algorithms are convergent. In order to prove global convergence results we use the majorization method. We also derive, for the first time, some quantitative convergence theorems, which give information about the speed of convergence. It turns out that in almost all cases convergence is linear, with a convergence rate close to unity. This has the practical consequence that convergence will usually be very slow, and this makes techniques to speed up convergence very important. It is pointed out that step-size techniques will generally not succeed in producing marked improvements in this respect.

Keywords

Multidimensional scalingConvergenceStep sizeLocal minima

Copyright information

© Springer-Verlag New York Inc. 1988

Authors and Affiliations

  • Jan de Leeuw
    • 1
  1. 1.Departments of Psychology and MathematicsUniversity of California Los AngelesLos AngelesUSA